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{ |
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"files.associations": { |
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"structures.h": "c" |
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} |
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} |
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YOLO LICENSE |
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Version 2, July 29 2016 |
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THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER |
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SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN |
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TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES |
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LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY. NOW HERE'S |
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0. Darknet is public domain. |
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1. Do whatever you want with it. |
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2. Stop emailing me about it! |
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DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE |
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Version 2, December 2004 |
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Copyright (C) 2004 Sam Hocevar <sam@hocevar.net> |
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Everyone is permitted to copy and distribute verbatim or modified |
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as the name is changed. |
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DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE |
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0. You just DO WHAT THE FUCK YOU WANT TO. |
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RNN LICENSE Version 3, June 21 2017 |
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GNU GENERAL PUBLIC LICENSE |
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||||
|
||||
The requirement to provide Installation Information does not include a |
||||
requirement to continue to provide support service, warranty, or updates |
||||
for a work that has been modified or installed by the recipient, or for |
||||
the User Product in which it has been modified or installed. Access to a |
||||
network may be denied when the modification itself materially and |
||||
adversely affects the operation of the network or violates the rules and |
||||
protocols for communication across the network. |
||||
|
||||
Corresponding Source conveyed, and Installation Information provided, |
||||
in accord with this section must be in a format that is publicly |
||||
documented (and with an implementation available to the public in |
||||
source code form), and must require no special password or key for |
||||
unpacking, reading or copying. |
||||
|
||||
7. Additional Terms. |
||||
|
||||
"Additional permissions" are terms that supplement the terms of this |
||||
License by making exceptions from one or more of its conditions. |
||||
Additional permissions that are applicable to the entire Program shall |
||||
be treated as though they were included in this License, to the extent |
||||
that they are valid under applicable law. If additional permissions |
||||
apply only to part of the Program, that part may be used separately |
||||
under those permissions, but the entire Program remains governed by |
||||
this License without regard to the additional permissions. |
||||
|
||||
When you convey a copy of a covered work, you may at your option |
||||
remove any additional permissions from that copy, or from any part of |
||||
it. (Additional permissions may be written to require their own |
||||
removal in certain cases when you modify the work.) You may place |
||||
additional permissions on material, added by you to a covered work, |
||||
for which you have or can give appropriate copyright permission. |
||||
|
||||
Notwithstanding any other provision of this License, for material you |
||||
add to a covered work, you may (if authorized by the copyright holders of |
||||
that material) supplement the terms of this License with terms: |
||||
|
||||
a) Disclaiming warranty or limiting liability differently from the |
||||
terms of sections 15 and 16 of this License; or |
||||
|
||||
b) Requiring preservation of specified reasonable legal notices or |
||||
author attributions in that material or in the Appropriate Legal |
||||
Notices displayed by works containing it; or |
||||
|
||||
c) Prohibiting misrepresentation of the origin of that material, or |
||||
requiring that modified versions of such material be marked in |
||||
reasonable ways as different from the original version; or |
||||
|
||||
d) Limiting the use for publicity purposes of names of licensors or |
||||
authors of the material; or |
||||
|
||||
e) Declining to grant rights under trademark law for use of some |
||||
trade names, trademarks, or service marks; or |
||||
|
||||
f) Requiring indemnification of licensors and authors of that |
||||
material by anyone who conveys the material (or modified versions of |
||||
it) with contractual assumptions of liability to the recipient, for |
||||
any liability that these contractual assumptions directly impose on |
||||
those licensors and authors. |
||||
|
||||
All other non-permissive additional terms are considered "further |
||||
restrictions" within the meaning of section 10. If the Program as you |
||||
received it, or any part of it, contains a notice stating that it is |
||||
governed by this License along with a term that is a further |
||||
restriction, you may remove that term. If a license document contains |
||||
a further restriction but permits relicensing or conveying under this |
||||
License, you may add to a covered work material governed by the terms |
||||
of that license document, provided that the further restriction does |
||||
not survive such relicensing or conveying. |
||||
|
||||
If you add terms to a covered work in accord with this section, you |
||||
must place, in the relevant source files, a statement of the |
||||
additional terms that apply to those files, or a notice indicating |
||||
where to find the applicable terms. |
||||
|
||||
Additional terms, permissive or non-permissive, may be stated in the |
||||
form of a separately written license, or stated as exceptions; |
||||
the above requirements apply either way. |
||||
|
||||
8. Termination. |
||||
|
||||
You may not propagate or modify a covered work except as expressly |
||||
provided under this License. Any attempt otherwise to propagate or |
||||
modify it is void, and will automatically terminate your rights under |
||||
this License (including any patent licenses granted under the third |
||||
paragraph of section 11). |
||||
|
||||
However, if you cease all violation of this License, then your |
||||
license from a particular copyright holder is reinstated (a) |
||||
provisionally, unless and until the copyright holder explicitly and |
||||
finally terminates your license, and (b) permanently, if the copyright |
||||
holder fails to notify you of the violation by some reasonable means |
||||
prior to 60 days after the cessation. |
||||
|
||||
Moreover, your license from a particular copyright holder is |
||||
reinstated permanently if the copyright holder notifies you of the |
||||
violation by some reasonable means, this is the first time you have |
||||
received notice of violation of this License (for any work) from that |
||||
copyright holder, and you cure the violation prior to 30 days after |
||||
your receipt of the notice. |
||||
|
||||
Termination of your rights under this section does not terminate the |
||||
licenses of parties who have received copies or rights from you under |
||||
this License. If your rights have been terminated and not permanently |
||||
reinstated, you do not qualify to receive new licenses for the same |
||||
material under section 10. |
||||
|
||||
9. Acceptance Not Required for Having Copies. |
||||
|
||||
You are not required to accept this License in order to receive or |
||||
run a copy of the Program. Ancillary propagation of a covered work |
||||
occurring solely as a consequence of using peer-to-peer transmission |
||||
to receive a copy likewise does not require acceptance. However, |
||||
nothing other than this License grants you permission to propagate or |
||||
modify any covered work. These actions infringe copyright if you do |
||||
not accept this License. Therefore, by modifying or propagating a |
||||
covered work, you indicate your acceptance of this License to do so. |
||||
|
||||
10. Automatic Licensing of Downstream Recipients. |
||||
|
||||
Each time you convey a covered work, the recipient automatically |
||||
receives a license from the original licensors, to run, modify and |
||||
propagate that work, subject to this License. You are not responsible |
||||
for enforcing compliance by third parties with this License. |
||||
|
||||
An "entity transaction" is a transaction transferring control of an |
||||
organization, or substantially all assets of one, or subdividing an |
||||
organization, or merging organizations. If propagation of a covered |
||||
work results from an entity transaction, each party to that |
||||
transaction who receives a copy of the work also receives whatever |
||||
licenses to the work the party's predecessor in interest had or could |
||||
give under the previous paragraph, plus a right to possession of the |
||||
Corresponding Source of the work from the predecessor in interest, if |
||||
the predecessor has it or can get it with reasonable efforts. |
||||
|
||||
You may not impose any further restrictions on the exercise of the |
||||
rights granted or affirmed under this License. For example, you may |
||||
not impose a license fee, royalty, or other charge for exercise of |
||||
rights granted under this License, and you may not initiate litigation |
||||
(including a cross-claim or counterclaim in a lawsuit) alleging that |
||||
any patent claim is infringed by making, using, selling, offering for |
||||
sale, or importing the Program or any portion of it. |
||||
|
||||
11. Patents. |
||||
|
||||
A "contributor" is a copyright holder who authorizes use under this |
||||
License of the Program or a work on which the Program is based. The |
||||
work thus licensed is called the contributor's "contributor version". |
||||
|
||||
A contributor's "essential patent claims" are all patent claims |
||||
owned or controlled by the contributor, whether already acquired or |
||||
hereafter acquired, that would be infringed by some manner, permitted |
||||
by this License, of making, using, or selling its contributor version, |
||||
but do not include claims that would be infringed only as a |
||||
consequence of further modification of the contributor version. For |
||||
purposes of this definition, "control" includes the right to grant |
||||
patent sublicenses in a manner consistent with the requirements of |
||||
this License. |
||||
|
||||
Each contributor grants you a non-exclusive, worldwide, royalty-free |
||||
patent license under the contributor's essential patent claims, to |
||||
make, use, sell, offer for sale, import and otherwise run, modify and |
||||
propagate the contents of its contributor version. |
||||
|
||||
In the following three paragraphs, a "patent license" is any express |
||||
agreement or commitment, however denominated, not to enforce a patent |
||||
(such as an express permission to practice a patent or covenant not to |
||||
sue for patent infringement). To "grant" such a patent license to a |
||||
party means to make such an agreement or commitment not to enforce a |
||||
patent against the party. |
||||
|
||||
If you convey a covered work, knowingly relying on a patent license, |
||||
and the Corresponding Source of the work is not available for anyone |
||||
to copy, free of charge and under the terms of this License, through a |
||||
publicly available network server or other readily accessible means, |
||||
then you must either (1) cause the Corresponding Source to be so |
||||
available, or (2) arrange to deprive yourself of the benefit of the |
||||
patent license for this particular work, or (3) arrange, in a manner |
||||
consistent with the requirements of this License, to extend the patent |
||||
license to downstream recipients. "Knowingly relying" means you have |
||||
actual knowledge that, but for the patent license, your conveying the |
||||
covered work in a country, or your recipient's use of the covered work |
||||
in a country, would infringe one or more identifiable patents in that |
||||
country that you have reason to believe are valid. |
||||
|
||||
If, pursuant to or in connection with a single transaction or |
||||
arrangement, you convey, or propagate by procuring conveyance of, a |
||||
covered work, and grant a patent license to some of the parties |
||||
receiving the covered work authorizing them to use, propagate, modify |
||||
or convey a specific copy of the covered work, then the patent license |
||||
you grant is automatically extended to all recipients of the covered |
||||
work and works based on it. |
||||
|
||||
A patent license is "discriminatory" if it does not include within |
||||
the scope of its coverage, prohibits the exercise of, or is |
||||
conditioned on the non-exercise of one or more of the rights that are |
||||
specifically granted under this License. You may not convey a covered |
||||
work if you are a party to an arrangement with a third party that is |
||||
in the business of distributing software, under which you make payment |
||||
to the third party based on the extent of your activity of conveying |
||||
the work, and under which the third party grants, to any of the |
||||
parties who would receive the covered work from you, a discriminatory |
||||
patent license (a) in connection with copies of the covered work |
||||
conveyed by you (or copies made from those copies), or (b) primarily |
||||
for and in connection with specific products or compilations that |
||||
contain the covered work, unless you entered into that arrangement, |
||||
or that patent license was granted, prior to 28 March 2007. |
||||
|
||||
Nothing in this License shall be construed as excluding or limiting |
||||
any implied license or other defenses to infringement that may |
||||
otherwise be available to you under applicable patent law. |
||||
|
||||
12. No Surrender of Others' Freedom. |
||||
|
||||
If conditions are imposed on you (whether by court order, agreement or |
||||
otherwise) that contradict the conditions of this License, they do not |
||||
excuse you from the conditions of this License. If you cannot convey a |
||||
covered work so as to satisfy simultaneously your obligations under this |
||||
License and any other pertinent obligations, then as a consequence you may |
||||
not convey it at all. For example, if you agree to terms that obligate you |
||||
to collect a royalty for further conveying from those to whom you convey |
||||
the Program, the only way you could satisfy both those terms and this |
||||
License would be to refrain entirely from conveying the Program. |
||||
|
||||
13. Use with the GNU Affero General Public License. |
||||
|
||||
Notwithstanding any other provision of this License, you have |
||||
permission to link or combine any covered work with a work licensed |
||||
under version 3 of the GNU Affero General Public License into a single |
||||
combined work, and to convey the resulting work. The terms of this |
||||
License will continue to apply to the part which is the covered work, |
||||
but the special requirements of the GNU Affero General Public License, |
||||
section 13, concerning interaction through a network will apply to the |
||||
combination as such. |
||||
|
||||
14. Revised Versions of this License. |
||||
|
||||
The Free Software Foundation may publish revised and/or new versions of |
||||
the GNU General Public License from time to time. Such new versions will |
||||
be similar in spirit to the present version, but may differ in detail to |
||||
address new problems or concerns. |
||||
|
||||
Each version is given a distinguishing version number. If the |
||||
Program specifies that a certain numbered version of the GNU General |
||||
Public License "or any later version" applies to it, you have the |
||||
option of following the terms and conditions either of that numbered |
||||
version or of any later version published by the Free Software |
||||
Foundation. If the Program does not specify a version number of the |
||||
GNU General Public License, you may choose any version ever published |
||||
by the Free Software Foundation. |
||||
|
||||
If the Program specifies that a proxy can decide which future |
||||
versions of the GNU General Public License can be used, that proxy's |
||||
public statement of acceptance of a version permanently authorizes you |
||||
to choose that version for the Program. |
||||
|
||||
Later license versions may give you additional or different |
||||
permissions. However, no additional obligations are imposed on any |
||||
author or copyright holder as a result of your choosing to follow a |
||||
later version. |
||||
|
||||
15. Disclaimer of Warranty. |
||||
|
||||
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY |
||||
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT |
||||
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY |
||||
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, |
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR |
||||
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM |
||||
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF |
||||
ALL NECESSARY SERVICING, REPAIR OR CORRECTION. |
||||
|
||||
16. Limitation of Liability. |
||||
|
||||
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING |
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS |
||||
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY |
||||
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE |
||||
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF |
||||
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD |
||||
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), |
||||
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF |
||||
SUCH DAMAGES. |
||||
|
||||
17. Interpretation of Sections 15 and 16. |
||||
|
||||
If the disclaimer of warranty and limitation of liability provided |
||||
above cannot be given local legal effect according to their terms, |
||||
reviewing courts shall apply local law that most closely approximates |
||||
an absolute waiver of all civil liability in connection with the |
||||
Program, unless a warranty or assumption of liability accompanies a |
||||
copy of the Program in return for a fee. |
||||
|
||||
END OF TERMS AND CONDITIONS |
||||
|
||||
How to Apply These Terms to Your New Programs |
||||
|
||||
If you develop a new program, and you want it to be of the greatest |
||||
possible use to the public, the best way to achieve this is to make it |
||||
free software which everyone can redistribute and change under these terms. |
||||
|
||||
To do so, attach the following notices to the program. It is safest |
||||
to attach them to the start of each source file to most effectively |
||||
state the exclusion of warranty; and each file should have at least |
||||
the "copyright" line and a pointer to where the full notice is found. |
||||
|
||||
{one line to give the program's name and a brief idea of what it does.} |
||||
Copyright (C) {year} {name of author} |
||||
|
||||
This program is free software: you can redistribute it and/or modify |
||||
it under the terms of the GNU General Public License as published by |
||||
the Free Software Foundation, either version 3 of the License, or |
||||
(at your option) any later version. |
||||
|
||||
This program is distributed in the hope that it will be useful, |
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of |
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
||||
GNU General Public License for more details. |
||||
|
||||
You should have received a copy of the GNU General Public License |
||||
along with this program. If not, see <http://www.gnu.org/licenses/>. |
||||
|
||||
Also add information on how to contact you by electronic and paper mail. |
||||
|
||||
If the program does terminal interaction, make it output a short |
||||
notice like this when it starts in an interactive mode: |
||||
|
||||
{project} Copyright (C) {year} {fullname} |
||||
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. |
||||
This is free software, and you are welcome to redistribute it |
||||
under certain conditions; type `show c' for details. |
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate |
||||
parts of the General Public License. Of course, your program's commands |
||||
might be different; for a GUI interface, you would use an "about box". |
||||
|
||||
You should also get your employer (if you work as a programmer) or school, |
||||
if any, to sign a "copyright disclaimer" for the program, if necessary. |
||||
For more information on this, and how to apply and follow the GNU GPL, see |
||||
<http://www.gnu.org/licenses/>. |
||||
|
||||
The GNU General Public License does not permit incorporating your program |
||||
into proprietary programs. If your program is a subroutine library, you |
||||
may consider it more useful to permit linking proprietary applications with |
||||
the library. If this is what you want to do, use the GNU Lesser General |
||||
Public License instead of this License. But first, please read |
||||
<http://www.gnu.org/philosophy/why-not-lgpl.html>. |
||||
@ -0,0 +1,8 @@
|
||||
META-LICENSE |
||||
Version 1, June 21 2017 |
||||
|
||||
Any and all licenses may be applied to the software either individually |
||||
or in concert. Any issues, ambiguities, paradoxes, or metaphysical quandries |
||||
arising from this combination should be discussed with a local faith leader, |
||||
hermit, or guru. The Oxford comma shall be used. |
||||
|
||||
@ -0,0 +1,22 @@
|
||||
MIT License |
||||
|
||||
Copyright (c) 2017 Joseph Redmon |
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy |
||||
of this software and associated documentation files (the "Software"), to deal |
||||
in the Software without restriction, including without limitation the rights |
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
||||
copies of the Software, and to permit persons to whom the Software is |
||||
furnished to do so, subject to the following conditions: |
||||
|
||||
The above copyright notice and this permission notice shall be included in all |
||||
copies or substantial portions of the Software. |
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
||||
SOFTWARE. |
||||
|
||||
@ -0,0 +1,13 @@
|
||||
YOLO LICENSE |
||||
Version 1, July 10 2015 |
||||
|
||||
THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER |
||||
SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN |
||||
TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES |
||||
LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY SUBJECT TO |
||||
THE FOLLOWING CONDITIONS: |
||||
|
||||
1. #yolo |
||||
2. #swag |
||||
3. #blazeit |
||||
|
||||
@ -0,0 +1,75 @@
|
||||
|
||||
GPU=0
|
||||
CUDNN=0
|
||||
OPENCV=0
|
||||
OPENMP=0
|
||||
DEBUG=0
|
||||
|
||||
OS := $(shell uname)
|
||||
|
||||
#VPATH=./src/:./examples
|
||||
VPATH=./src
|
||||
#SLIB= libopencv_core.so libopencv_highgui.so
|
||||
ALIB=libdarknet.a
|
||||
EXEC=gynet
|
||||
OBJDIR=./obj/
|
||||
|
||||
#-I/usr/local/opencv/include
|
||||
#-L/usr/local/opencv/lib
|
||||
|
||||
TOOLCHAIN_PATH ?= /usr/local/linaro-aarch64-2018.08-gcc8.2
|
||||
CROSS_COMPILE_TOOL_CHAIN_PATH := $(TOOLCHAIN_PATH)
|
||||
CC = $(CROSS_COMPILE_TOOL_CHAIN_PATH)/bin/aarch64-linux-gnu-gcc
|
||||
CPP = $(CROSS_COMPILE_TOOL_CHAIN_PATH)/bin/aarch64-linux-gnu-g++
|
||||
|
||||
#CC=gcc
|
||||
#CPP=g++
|
||||
NVCC=nvcc
|
||||
AR=ar
|
||||
ARFLAGS=rcs
|
||||
OPTS=-Ofast
|
||||
LDFLAGS= -lm -pthread -L$(PWD) -Wl,--start-group libcurl.a libssl.a libcrypto.a libz.a -lpng16 -ldl -lpthread libjpeg.a libpng16.a libopencv_core.a libopencv_imgproc.a libopencv_imgcodecs.a libopencv_video.a libopencv_img_hash.a -Wl,--end-group
|
||||
#COMMON= -Iinclude/ -Isrc/
|
||||
COMMON= -Iinclude/ -Isrc/ -Isrc/opencv/ -Isrc/opencv2/ -Iinclude/ambarella/ -Iinclude/ambarella/arch_v5/
|
||||
CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC
|
||||
|
||||
CFLAGS+=$(OPTS)
|
||||
|
||||
OBJ=gemm.o utility.o utils.o cJSON.o cryptionPlus.o nweb.o strptime_c.o gettest.o exif.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o image_opencv.o art.o detector.o darknet.o test_nnctrl_live.o amba_bbox_utils.o amba_ssd_detection_out.o amba_yolov3_out.o lib_data_process.o signal_test.o iniReader.o alm_queue.o sqlite_db.o fflpr_plate_db.o cgicmd.o encrypt.o decrypt.o ColorDetector.o base64.o intLib.o sha1.o websocket.o ptz.o pns.o md5.o cold_zone.o pns.o net_curl.o ivs.o fork_pipe_lib.o cv_point_transform.o block_to_send.o md5_f.o url_encode.o send_osd_data.o ir_control.o dbscan.o levenshtein.o levenshtein_sqlite.o yuv_rgb.o test_yuv_rgb.o structures.o barcode.o onvif_data.o ivs_detection.o pythonR.o anpr_rule.o k_means.o parking_method.o
|
||||
|
||||
|
||||
#osd_server_yolov3.o osd_server_utils.o lib_smartfb.o
|
||||
#captcha.o yolo.o cifar.o classifier.o coco.o go.o instance-segmenter.o lsd.o nightmare.o regressor.o
|
||||
#segmenter.o super.o
|
||||
OBJS = $(addprefix $(OBJDIR), $(OBJ))
|
||||
DEPS = $(wildcard src/*.h) $(wildcard src/*.hpp) $(wildcard src/opencv/*.h) $(wildcard src/opencv/*.hpp) $(wildcard src/opencv2/*.h) $(wildcard src/opencv2/*.hpp) $(wildcard include/ambarella/freetype/*.h) $(wildcard include/ambarella/freetype/config/*.h)
|
||||
|
||||
#all: obj backup results $(ALIB) $(EXEC)
|
||||
all: obj results $(ALIB) $(EXEC) libnnctrl.so #lib_data_process.so
|
||||
|
||||
$(EXEC): $(ALIB) |
||||
$(CC) -g $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) libzlog.a -lstdc++ $(ALIB) \
|
||||
libnnctrl.so libcavalry_mem.so libvproc.so libfreetype.so libsqlite3.so libidn2.so.0.3.5 libnghttp2.so.14.13.3 libunistring.so.2.1.0 libzbar.so.0.2.0
|
||||
#lib_data_process.so
|
||||
|
||||
$(ALIB): $(OBJS) |
||||
$(AR) $(ARFLAGS) $@ $^
|
||||
|
||||
$(OBJDIR)%.o: %.cpp $(DEPS) |
||||
$(CPP) $(COMMON) $(CFLAGS) -c $< -o $@
|
||||
|
||||
$(OBJDIR)%.o: %.c $(DEPS) |
||||
$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
|
||||
|
||||
obj: |
||||
mkdir -p obj
|
||||
backup: |
||||
mkdir -p backup
|
||||
results: |
||||
mkdir -p results
|
||||
|
||||
.PHONY: clean |
||||
|
||||
clean: |
||||
rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*
|
||||
|
||||
|
After Width: | Height: | Size: 91 KiB |
@ -0,0 +1,96 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=1 |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
height=227 |
||||
width=227 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
max_crop=256 |
||||
|
||||
learning_rate=0.01 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
|
||||
angle=7 |
||||
hue = .1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
[convolutional] |
||||
filters=96 |
||||
size=11 |
||||
stride=4 |
||||
pad=0 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=3 |
||||
stride=2 |
||||
padding=0 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=5 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=3 |
||||
stride=2 |
||||
padding=0 |
||||
|
||||
[convolutional] |
||||
filters=384 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=384 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=3 |
||||
stride=2 |
||||
padding=0 |
||||
|
||||
[connected] |
||||
output=4096 |
||||
activation=relu |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[connected] |
||||
output=4096 |
||||
activation=relu |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[connected] |
||||
output=1000 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,121 @@
|
||||
[net] |
||||
batch=128 |
||||
subdivisions=1 |
||||
height=28 |
||||
width=28 |
||||
channels=3 |
||||
max_crop=32 |
||||
min_crop=32 |
||||
|
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
|
||||
learning_rate=0.4 |
||||
policy=poly |
||||
power=4 |
||||
max_batches = 5000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[convolutional] |
||||
filters=10 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
@ -0,0 +1,117 @@
|
||||
[net] |
||||
batch=128 |
||||
subdivisions=1 |
||||
height=32 |
||||
width=32 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.4 |
||||
policy=poly |
||||
power=4 |
||||
max_batches = 50000 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[convolutional] |
||||
filters=10 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
temperature=3 |
||||
|
||||
@ -0,0 +1,8 @@
|
||||
classes= 80 |
||||
train = /home/pjreddie/data/coco/trainvalno5k.txt |
||||
valid = coco_testdev |
||||
#valid = data/coco_val_5k.list |
||||
names = data/coco.names |
||||
backup = /home/pjreddie/backup/ |
||||
eval=coco |
||||
|
||||
@ -0,0 +1,10 @@
|
||||
classes= 9418 |
||||
#train = /home/pjreddie/data/coco/trainvalno5k.txt |
||||
train = data/combine9k.train.list |
||||
valid = /home/pjreddie/data/imagenet/det.val.files |
||||
labels = data/9k.labels |
||||
names = data/9k.names |
||||
backup = backup/ |
||||
map = data/inet9k.map |
||||
eval = imagenet |
||||
results = results |
||||
@ -0,0 +1,120 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=1 |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
height=256 |
||||
width=256 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[avgpool] |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,205 @@
|
||||
[net] |
||||
# Training |
||||
#batch=128 |
||||
#subdivisions=2 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,197 @@
|
||||
[net] |
||||
batch=128 |
||||
subdivisions=4 |
||||
height=448 |
||||
width=448 |
||||
max_crop=512 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.001 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=100000 |
||||
|
||||
angle=7 |
||||
hue = .1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,566 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=4 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[avgpool] |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,559 @@
|
||||
[net] |
||||
# Training - start training with darknet53.weights |
||||
# batch=128 |
||||
# subdivisions=8 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=448 |
||||
width=448 |
||||
channels=3 |
||||
min_crop=448 |
||||
max_crop=512 |
||||
|
||||
learning_rate=0.001 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=100000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[avgpool] |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,205 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=4 |
||||
# Testing |
||||
batch = 1 |
||||
subdivisions = 1 |
||||
height=448 |
||||
width=448 |
||||
max_crop=512 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.001 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=100000 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=9418 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
tree=data/9k.tree |
||||
|
||||
[cost] |
||||
type=masked |
||||
|
||||
@ -0,0 +1,209 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=4 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=224 |
||||
width=224 |
||||
max_crop=320 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=1600000 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=192 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,179 @@
|
||||
[net] |
||||
batch=1 |
||||
subdivisions=1 |
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.5 |
||||
policy=poly |
||||
power=6 |
||||
max_batches=500000 |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=192 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[avgpool] |
||||
|
||||
[connected] |
||||
output=1000 |
||||
activation=leaky |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,206 @@
|
||||
[net] |
||||
batch=128 |
||||
subdivisions=1 |
||||
height=224 |
||||
width=224 |
||||
max_crop=320 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.01 |
||||
max_batches = 0 |
||||
policy=steps |
||||
steps=444000,590000,970000 |
||||
scales=.5,.2,.1 |
||||
|
||||
#policy=sigmoid |
||||
#gamma=.00008 |
||||
#step=100000 |
||||
#max_batches=200000 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=192 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[avgpool] |
||||
|
||||
[connected] |
||||
output=21842 |
||||
activation=leaky |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,132 @@
|
||||
[net] |
||||
batch=512 |
||||
subdivisions=1 |
||||
height=19 |
||||
width=19 |
||||
channels=1 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=10000000 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=1 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[reorg] |
||||
extra=1 |
||||
stride=1 |
||||
|
||||
[softmax] |
||||
|
||||
@ -0,0 +1,132 @@
|
||||
[net] |
||||
batch=1 |
||||
subdivisions=1 |
||||
height=19 |
||||
width=19 |
||||
channels=1 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.01 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=100000 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
batch_normalize=1 |
||||
|
||||
[convolutional] |
||||
filters=1 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[reorg] |
||||
extra=1 |
||||
stride=1 |
||||
|
||||
[softmax] |
||||
|
||||
|
||||
@ -0,0 +1,29 @@
|
||||
[net] |
||||
inputs=256 |
||||
momentum=0.9 |
||||
decay=0.0 |
||||
subdivisions=1 |
||||
batch = 1 |
||||
time_steps=1 |
||||
learning_rate=.002 |
||||
adam=1 |
||||
|
||||
policy=constant |
||||
power=4 |
||||
max_batches=1000000 |
||||
|
||||
[gru] |
||||
output = 256 |
||||
|
||||
[gru] |
||||
output = 256 |
||||
|
||||
[gru] |
||||
output = 256 |
||||
|
||||
[connected] |
||||
output=256 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
|
||||
@ -0,0 +1,8 @@
|
||||
classes=1000 |
||||
train = /data/imagenet/imagenet1k.train.list |
||||
valid = /data/imagenet/imagenet1k.valid.list |
||||
backup = /home/pjreddie/backup/ |
||||
labels = data/imagenet.labels.list |
||||
names = data/imagenet.shortnames.list |
||||
top=5 |
||||
|
||||
@ -0,0 +1,9 @@
|
||||
classes=21842 |
||||
train = /data/imagenet/imagenet22k.train.list |
||||
valid = /data/imagenet/imagenet22k.valid.list |
||||
#valid = /data/imagenet/imagenet1k.valid.list |
||||
backup = /home/pjreddie/backup/ |
||||
labels = data/imagenet.labels.list |
||||
names = data/imagenet.shortnames.list |
||||
top = 5 |
||||
|
||||
@ -0,0 +1,9 @@
|
||||
classes=9418 |
||||
train = data/9k.train.list |
||||
valid = /data/imagenet/imagenet1k.valid.list |
||||
leaves = data/imagenet1k.labels |
||||
backup = /home/pjreddie/backup/ |
||||
labels = data/9k.labels |
||||
names = data/9k.names |
||||
top=5 |
||||
|
||||
@ -0,0 +1,118 @@
|
||||
[net] |
||||
batch=1 |
||||
subdivisions=1 |
||||
height=10 |
||||
width=10 |
||||
channels=3 |
||||
learning_rate=0.01 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
[convolutional] |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
stride=2 |
||||
size=2 |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
stride=2 |
||||
size=2 |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
stride=2 |
||||
size=2 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
stride=2 |
||||
size=2 |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
stride=2 |
||||
size=2 |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
@ -0,0 +1,8 @@
|
||||
classes= 601 |
||||
train = /home/pjreddie/data/openimsv4/openimages.train.list |
||||
#valid = coco_testdev |
||||
valid = data/coco_val_5k.list |
||||
names = data/openimages.names |
||||
backup = /home/pjreddie/backup/ |
||||
eval=coco |
||||
|
||||
@ -0,0 +1,990 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=2 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
|
||||
# Conv 4 |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
#Conv 5 |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
[cost] |
||||
type=sse |
||||
|
||||
@ -0,0 +1,228 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=1 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Strided Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
|
||||
# Strided Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
|
||||
# Strided Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
|
||||
|
||||
|
||||
[avgpool] |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,392 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=2 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Strided Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Strided Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
# Residual Block |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
activation=leaky |
||||
from=-3 |
||||
|
||||
|
||||
|
||||
[avgpool] |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
@ -0,0 +1,510 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=4 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
|
||||
# Conv 4 |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
#Conv 5 |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
|
||||
|
||||
|
||||
|
||||
[avgpool] |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
|
||||
@ -0,0 +1,523 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=4 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
min_crop=128 |
||||
max_crop=448 |
||||
|
||||
burn_in=1000 |
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=800000 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
groups=32 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
|
||||
# Conv 4 |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
groups=32 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
#Conv 5 |
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
groups=32 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
groups=32 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=2048 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[shortcut] |
||||
from=-4 |
||||
activation=leaky |
||||
|
||||
[avgpool] |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
|
||||
@ -0,0 +1,38 @@
|
||||
[net] |
||||
subdivisions=1 |
||||
inputs=256 |
||||
batch = 1 |
||||
momentum=0.9 |
||||
decay=0.001 |
||||
max_batches = 2000 |
||||
time_steps=1 |
||||
learning_rate=0.1 |
||||
policy=steps |
||||
steps=1000,1500 |
||||
scales=.1,.1 |
||||
|
||||
[rnn] |
||||
batch_normalize=1 |
||||
output = 1024 |
||||
hidden=1024 |
||||
activation=leaky |
||||
|
||||
[rnn] |
||||
batch_normalize=1 |
||||
output = 1024 |
||||
hidden=1024 |
||||
activation=leaky |
||||
|
||||
[rnn] |
||||
batch_normalize=1 |
||||
output = 1024 |
||||
hidden=1024 |
||||
activation=leaky |
||||
|
||||
[connected] |
||||
output=256 |
||||
activation=leaky |
||||
|
||||
[softmax] |
||||
|
||||
|
||||
@ -0,0 +1,38 @@
|
||||
[net] |
||||
subdivisions=1 |
||||
inputs=256 |
||||
batch = 128 |
||||
momentum=0.9 |
||||
decay=0.001 |
||||
max_batches = 2000 |
||||
time_steps=576 |
||||
learning_rate=0.1 |
||||
policy=steps |
||||
steps=1000,1500 |
||||
scales=.1,.1 |
||||
|
||||
[rnn] |
||||
batch_normalize=1 |
||||
output = 1024 |
||||
hidden=1024 |
||||
activation=leaky |
||||
|
||||
[rnn] |
||||
batch_normalize=1 |
||||
output = 1024 |
||||
hidden=1024 |
||||
activation=leaky |
||||
|
||||
[rnn] |
||||
batch_normalize=1 |
||||
output = 1024 |
||||
hidden=1024 |
||||
activation=leaky |
||||
|
||||
[connected] |
||||
output=256 |
||||
activation=leaky |
||||
|
||||
[softmax] |
||||
|
||||
|
||||
@ -0,0 +1,182 @@
|
||||
[net] |
||||
batch=128 |
||||
subdivisions=4 |
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.01 |
||||
policy=steps |
||||
scales=.1,.1,.1 |
||||
steps=200000,300000,400000 |
||||
max_batches=800000 |
||||
|
||||
|
||||
[crop] |
||||
crop_height=224 |
||||
crop_width=224 |
||||
flip=1 |
||||
angle=0 |
||||
saturation=1 |
||||
exposure=1 |
||||
shift=.2 |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=192 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=ramp |
||||
|
||||
[maxpool] |
||||
size=3 |
||||
stride=2 |
||||
|
||||
[connected] |
||||
output=4096 |
||||
activation=ramp |
||||
|
||||
[dropout] |
||||
probability=0.5 |
||||
|
||||
[connected] |
||||
output=1000 |
||||
activation=ramp |
||||
|
||||
[softmax] |
||||
|
||||
@ -0,0 +1,117 @@
|
||||
[net] |
||||
batch=1 |
||||
subdivisions=1 |
||||
height=224 |
||||
width=224 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.0005 |
||||
policy=steps |
||||
steps=200,400,600,20000,30000 |
||||
scales=2.5,2,2,.1,.1 |
||||
max_batches = 40000 |
||||
|
||||
[convolutional] |
||||
filters=16 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[connected] |
||||
output= 1470 |
||||
activation=linear |
||||
|
||||
[detection] |
||||
classes=20 |
||||
coords=4 |
||||
rescore=1 |
||||
side=7 |
||||
num=2 |
||||
softmax=0 |
||||
sqrt=1 |
||||
jitter=.2 |
||||
|
||||
object_scale=1 |
||||
noobject_scale=.5 |
||||
class_scale=1 |
||||
coord_scale=5 |
||||
|
||||
@ -0,0 +1,174 @@
|
||||
[net] |
||||
# Train |
||||
batch=128 |
||||
subdivisions=1 |
||||
# Test |
||||
# batch=1 |
||||
# subdivisions=1 |
||||
height=224 |
||||
width=224 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
max_crop=320 |
||||
|
||||
learning_rate=0.1 |
||||
policy=poly |
||||
power=4 |
||||
max_batches=1600000 |
||||
|
||||
angle=7 |
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
aspect=.75 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1000 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[avgpool] |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
|
||||
@ -0,0 +1,157 @@
|
||||
[net] |
||||
# Training |
||||
# batch=128 |
||||
# subdivisions=4 |
||||
|
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
|
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
learning_rate=0.00001 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
[crop] |
||||
crop_height=224 |
||||
crop_width=224 |
||||
flip=1 |
||||
exposure=1 |
||||
saturation=1 |
||||
angle=0 |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[connected] |
||||
output=4096 |
||||
activation=relu |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[connected] |
||||
output=4096 |
||||
activation=relu |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[connected] |
||||
output=1000 |
||||
activation=linear |
||||
|
||||
[softmax] |
||||
groups=1 |
||||
|
||||
|
||||
@ -0,0 +1,121 @@
|
||||
[net] |
||||
batch=1 |
||||
subdivisions=1 |
||||
width=224 |
||||
height=224 |
||||
channels=3 |
||||
learning_rate=0.00001 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[convolutional] |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=relu |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
@ -0,0 +1,6 @@
|
||||
classes= 20 |
||||
train = /home/pjreddie/data/voc/train.txt |
||||
valid = /home/pjreddie/data/voc/2007_test.txt |
||||
names = data/voc.names |
||||
backup = backup |
||||
|
||||
@ -0,0 +1,41 @@
|
||||
[net] |
||||
batch=128 |
||||
subdivisions=2 |
||||
height=256 |
||||
width=256 |
||||
channels=3 |
||||
learning_rate=0.00000001 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
seen=0 |
||||
|
||||
[convolutional] |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=logistic |
||||
|
||||
[cost] |
||||
|
||||
@ -0,0 +1,218 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=8 |
||||
batch=1 |
||||
subdivisions=1 |
||||
height=544 |
||||
width=544 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 500200 |
||||
policy=steps |
||||
steps=400000,450000 |
||||
scales=.1,.1 |
||||
|
||||
hue=.1 |
||||
saturation=.75 |
||||
exposure=.75 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
filters=28269 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=linear |
||||
|
||||
[region] |
||||
anchors = 0.77871, 1.14074, 3.00525, 4.31277, 9.22725, 9.61974 |
||||
bias_match=1 |
||||
classes=9418 |
||||
coords=4 |
||||
num=3 |
||||
softmax=1 |
||||
jitter=.2 |
||||
rescore=1 |
||||
|
||||
object_scale=5 |
||||
noobject_scale=1 |
||||
class_scale=1 |
||||
coord_scale=1 |
||||
|
||||
thresh = .6 |
||||
absolute=1 |
||||
random=1 |
||||
|
||||
tree=data/9k.tree |
||||
map = data/coco9k.map |
||||
@ -0,0 +1,130 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=8 |
||||
height=448 |
||||
width=448 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
|
||||
saturation=.75 |
||||
exposure=.75 |
||||
hue = .1 |
||||
|
||||
learning_rate=0.0005 |
||||
policy=steps |
||||
steps=200,400,600,800,20000,30000 |
||||
scales=2.5,2,2,2,.1,.1 |
||||
max_batches = 40000 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[connected] |
||||
output= 1470 |
||||
activation=linear |
||||
|
||||
[detection] |
||||
classes=20 |
||||
coords=4 |
||||
rescore=1 |
||||
side=7 |
||||
num=2 |
||||
softmax=0 |
||||
sqrt=1 |
||||
jitter=.2 |
||||
|
||||
object_scale=1 |
||||
noobject_scale=.5 |
||||
class_scale=1 |
||||
coord_scale=5 |
||||
|
||||
@ -0,0 +1,261 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=8 |
||||
height=448 |
||||
width=448 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
saturation=1.5 |
||||
exposure=1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.0005 |
||||
policy=steps |
||||
steps=200,400,600,20000,30000 |
||||
scales=2.5,2,2,.1,.1 |
||||
max_batches = 40000 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=7 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=192 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
####### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[local] |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[dropout] |
||||
probability=.5 |
||||
|
||||
[connected] |
||||
output= 1715 |
||||
activation=linear |
||||
|
||||
[detection] |
||||
classes=20 |
||||
coords=4 |
||||
rescore=1 |
||||
side=7 |
||||
num=3 |
||||
softmax=0 |
||||
sqrt=1 |
||||
jitter=.2 |
||||
|
||||
object_scale=1 |
||||
noobject_scale=.5 |
||||
class_scale=1 |
||||
coord_scale=5 |
||||
|
||||
@ -0,0 +1,138 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=2 |
||||
width=416 |
||||
height=416 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
max_batches = 40200 |
||||
policy=steps |
||||
steps=-1,100,20000,30000 |
||||
scales=.1,10,.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
########### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=125 |
||||
activation=linear |
||||
|
||||
[region] |
||||
anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 |
||||
bias_match=1 |
||||
classes=20 |
||||
coords=4 |
||||
num=5 |
||||
softmax=1 |
||||
jitter=.2 |
||||
rescore=1 |
||||
|
||||
object_scale=5 |
||||
noobject_scale=1 |
||||
class_scale=1 |
||||
coord_scale=1 |
||||
|
||||
absolute=1 |
||||
thresh = .6 |
||||
random=1 |
||||
@ -0,0 +1,139 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=2 |
||||
width=416 |
||||
height=416 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 500200 |
||||
policy=steps |
||||
steps=400000,450000 |
||||
scales=.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
########### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=425 |
||||
activation=linear |
||||
|
||||
[region] |
||||
anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828 |
||||
bias_match=1 |
||||
classes=80 |
||||
coords=4 |
||||
num=5 |
||||
softmax=1 |
||||
jitter=.2 |
||||
rescore=0 |
||||
|
||||
object_scale=5 |
||||
noobject_scale=1 |
||||
class_scale=1 |
||||
coord_scale=1 |
||||
|
||||
absolute=1 |
||||
thresh = .6 |
||||
random=1 |
||||
@ -0,0 +1,258 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=8 |
||||
height=416 |
||||
width=416 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 80200 |
||||
policy=steps |
||||
steps=40000,60000 |
||||
scales=.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
|
||||
####### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[route] |
||||
layers=-9 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=64 |
||||
activation=leaky |
||||
|
||||
[reorg] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers=-1,-4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=125 |
||||
activation=linear |
||||
|
||||
|
||||
[region] |
||||
anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071 |
||||
bias_match=1 |
||||
classes=20 |
||||
coords=4 |
||||
num=5 |
||||
softmax=1 |
||||
jitter=.3 |
||||
rescore=1 |
||||
|
||||
object_scale=5 |
||||
noobject_scale=1 |
||||
class_scale=1 |
||||
coord_scale=1 |
||||
|
||||
absolute=1 |
||||
thresh = .6 |
||||
random=1 |
||||
@ -0,0 +1,258 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=8 |
||||
width=608 |
||||
height=608 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 500200 |
||||
policy=steps |
||||
steps=400000,450000 |
||||
scales=.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
|
||||
####### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[route] |
||||
layers=-9 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=64 |
||||
activation=leaky |
||||
|
||||
[reorg] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers=-1,-4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=425 |
||||
activation=linear |
||||
|
||||
|
||||
[region] |
||||
anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828 |
||||
bias_match=1 |
||||
classes=80 |
||||
coords=4 |
||||
num=5 |
||||
softmax=1 |
||||
jitter=.3 |
||||
rescore=1 |
||||
|
||||
object_scale=5 |
||||
noobject_scale=1 |
||||
class_scale=1 |
||||
coord_scale=1 |
||||
|
||||
absolute=1 |
||||
thresh = .6 |
||||
random=1 |
||||
@ -0,0 +1,789 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
batch=64 |
||||
subdivisions=16 |
||||
width=608 |
||||
height=608 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=5000 |
||||
max_batches = 500200 |
||||
policy=steps |
||||
steps=400000,450000 |
||||
scales=.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
###################### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1818 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 6,7,8 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=601 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 61 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1818 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 3,4,5 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=601 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
|
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 36 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1818 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 0,1,2 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=601 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
@ -0,0 +1,822 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=16 |
||||
width=608 |
||||
height=608 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 500200 |
||||
policy=steps |
||||
steps=400000,450000 |
||||
scales=.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
###################### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
### SPP ### |
||||
[maxpool] |
||||
stride=1 |
||||
size=5 |
||||
|
||||
[route] |
||||
layers=-2 |
||||
|
||||
[maxpool] |
||||
stride=1 |
||||
size=9 |
||||
|
||||
[route] |
||||
layers=-4 |
||||
|
||||
[maxpool] |
||||
stride=1 |
||||
size=13 |
||||
|
||||
[route] |
||||
layers=-1,-3,-5,-6 |
||||
|
||||
### End SPP ### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 6,7,8 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=80 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 61 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 3,4,5 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=80 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
|
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 36 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 0,1,2 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=80 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
@ -0,0 +1,182 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=2 |
||||
width=416 |
||||
height=416 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 500200 |
||||
policy=steps |
||||
steps=400000,450000 |
||||
scales=.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=16 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=2 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[maxpool] |
||||
size=2 |
||||
stride=1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
########### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
|
||||
|
||||
[yolo] |
||||
mask = 3,4,5 |
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 |
||||
classes=80 |
||||
num=6 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 8 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
[yolo] |
||||
mask = 0,1,2 |
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 |
||||
classes=80 |
||||
num=6 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
@ -0,0 +1,785 @@
|
||||
[net] |
||||
# Testing |
||||
batch=1 |
||||
subdivisions=1 |
||||
# Training |
||||
# batch=64 |
||||
# subdivisions=16 |
||||
width=416 |
||||
height=416 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 50200 |
||||
policy=steps |
||||
steps=40000,45000 |
||||
scales=.1,.1 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
###################### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=75 |
||||
activation=linear |
||||
|
||||
[yolo] |
||||
mask = 6,7,8 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=20 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .5 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 61 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=75 |
||||
activation=linear |
||||
|
||||
[yolo] |
||||
mask = 3,4,5 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=20 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .5 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 36 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=75 |
||||
activation=linear |
||||
|
||||
[yolo] |
||||
mask = 0,1,2 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=20 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .5 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
@ -0,0 +1,789 @@
|
||||
[net] |
||||
# Testing |
||||
# batch=1 |
||||
# subdivisions=1 |
||||
# Training |
||||
batch=64 |
||||
subdivisions=16 |
||||
width=608 |
||||
height=608 |
||||
channels=3 |
||||
momentum=0.9 |
||||
decay=0.0005 |
||||
angle=0 |
||||
saturation = 1.5 |
||||
exposure = 1.5 |
||||
hue=.1 |
||||
|
||||
learning_rate=0.001 |
||||
burn_in=1000 |
||||
max_batches = 500200 |
||||
policy=steps |
||||
steps=400000,450000 |
||||
scales=.1,.1 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=32 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=64 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
# Downsample |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=2 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=1024 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[shortcut] |
||||
from=-3 |
||||
activation=linear |
||||
|
||||
###################### |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=512 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=1024 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 6,7,8 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=80 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 61 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=256 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=512 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 3,4,5 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=80 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
|
||||
|
||||
[route] |
||||
layers = -4 |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[upsample] |
||||
stride=2 |
||||
|
||||
[route] |
||||
layers = -1, 36 |
||||
|
||||
|
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
filters=128 |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
batch_normalize=1 |
||||
size=3 |
||||
stride=1 |
||||
pad=1 |
||||
filters=256 |
||||
activation=leaky |
||||
|
||||
[convolutional] |
||||
size=1 |
||||
stride=1 |
||||
pad=1 |
||||
filters=255 |
||||
activation=linear |
||||
|
||||
|
||||
[yolo] |
||||
mask = 0,1,2 |
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 |
||||
classes=80 |
||||
num=9 |
||||
jitter=.3 |
||||
ignore_thresh = .7 |
||||
truth_thresh = 1 |
||||
random=1 |
||||
|
||||
@ -0,0 +1,80 @@
|
||||
person |
||||
bicycle |
||||
car |
||||
motorbike |
||||
aeroplane |
||||
bus |
||||
train |
||||
truck |
||||
boat |
||||
traffic light |
||||
fire hydrant |
||||
stop sign |
||||
parking meter |
||||
bench |
||||
bird |
||||
cat |
||||
dog |
||||
horse |
||||
sheep |
||||
cow |
||||
elephant |
||||
bear |
||||
zebra |
||||
giraffe |
||||
backpack |
||||
umbrella |
||||
handbag |
||||
tie |
||||
suitcase |
||||
frisbee |
||||
skis |
||||
snowboard |
||||
sports ball |
||||
kite |
||||
baseball bat |
||||
baseball glove |
||||
skateboard |
||||
surfboard |
||||
tennis racket |
||||
bottle |
||||
wine glass |
||||
cup |
||||
fork |
||||
knife |
||||
spoon |
||||
bowl |
||||
banana |
||||
apple |
||||
sandwich |
||||
orange |
||||
broccoli |
||||
carrot |
||||
hot dog |
||||
pizza |
||||
donut |
||||
cake |
||||
chair |
||||
sofa |
||||
pottedplant |
||||
bed |
||||
diningtable |
||||
toilet |
||||
tvmonitor |
||||
laptop |
||||
mouse |
||||
remote |
||||
keyboard |
||||
cell phone |
||||
microwave |
||||
oven |
||||
toaster |
||||
sink |
||||
refrigerator |
||||
book |
||||
clock |
||||
vase |
||||
scissors |
||||
teddy bear |
||||
hair drier |
||||
toothbrush |
||||
@ -0,0 +1,80 @@
|
||||
5177 |
||||
3768 |
||||
3802 |
||||
3800 |
||||
4107 |
||||
4072 |
||||
4071 |
||||
3797 |
||||
4097 |
||||
2645 |
||||
5150 |
||||
2644 |
||||
3257 |
||||
2523 |
||||
6527 |
||||
6866 |
||||
6912 |
||||
7342 |
||||
7255 |
||||
7271 |
||||
7217 |
||||
6858 |
||||
7343 |
||||
7233 |
||||
3704 |
||||
4374 |
||||
3641 |
||||
5001 |
||||
3899 |
||||
2999 |
||||
2631 |
||||
5141 |
||||
2015 |
||||
1133 |
||||
1935 |
||||
1930 |
||||
5144 |
||||
5143 |
||||
2371 |
||||
3916 |
||||
3745 |
||||
3640 |
||||
4749 |
||||
4736 |
||||
4735 |
||||
3678 |
||||
58 |
||||
42 |
||||
771 |
||||
81 |
||||
152 |
||||
141 |
||||
786 |
||||
700 |
||||
218 |
||||
791 |
||||
2518 |
||||
2521 |
||||
3637 |
||||
2458 |
||||
2505 |
||||
2519 |
||||
3499 |
||||
2837 |
||||
3503 |
||||
2597 |
||||
3430 |
||||
2080 |
||||
5103 |
||||
5111 |
||||
5102 |
||||
3013 |
||||
5096 |
||||
1102 |
||||
3218 |
||||
4010 |
||||
2266 |
||||
1127 |
||||
5122 |
||||
2360 |
||||
|
After Width: | Height: | Size: 160 KiB |
|
After Width: | Height: | Size: 139 KiB |
|
After Width: | Height: | Size: 374 KiB |
@ -0,0 +1,3 @@
|
||||
+++++ |
||||
val_eq (Val.add (Val.add (r3 PC) Vone) Vone) (Val.add (x2 PC) Vone) |
||||
***** |
||||
|
After Width: | Height: | Size: 130 KiB |
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,200 @@
|
||||
2687 |
||||
4107 |
||||
8407 |
||||
7254 |
||||
42 |
||||
6797 |
||||
127 |
||||
2268 |
||||
2442 |
||||
3704 |
||||
260 |
||||
1970 |
||||
58 |
||||
4443 |
||||
2661 |
||||
2043 |
||||
2039 |
||||
4858 |
||||
4007 |
||||
6858 |
||||
8408 |
||||
166 |
||||
2523 |
||||
3768 |
||||
4347 |
||||
6527 |
||||
2446 |
||||
5005 |
||||
3274 |
||||
3678 |
||||
4918 |
||||
709 |
||||
4072 |
||||
8428 |
||||
7223 |
||||
2251 |
||||
3802 |
||||
3848 |
||||
7271 |
||||
2677 |
||||
8267 |
||||
2849 |
||||
2518 |
||||
2738 |
||||
3746 |
||||
5105 |
||||
3430 |
||||
3503 |
||||
2249 |
||||
1841 |
||||
2032 |
||||
2358 |
||||
122 |
||||
3984 |
||||
4865 |
||||
3246 |
||||
5095 |
||||
6912 |
||||
6878 |
||||
8467 |
||||
2741 |
||||
1973 |
||||
3057 |
||||
7217 |
||||
1872 |
||||
44 |
||||
2452 |
||||
3637 |
||||
2704 |
||||
6917 |
||||
2715 |
||||
6734 |
||||
2325 |
||||
6864 |
||||
6677 |
||||
2035 |
||||
1949 |
||||
338 |
||||
2664 |
||||
5122 |
||||
1844 |
||||
784 |
||||
2223 |
||||
7188 |
||||
2719 |
||||
2670 |
||||
4830 |
||||
158 |
||||
4818 |
||||
7228 |
||||
1965 |
||||
7342 |
||||
786 |
||||
2095 |
||||
8281 |
||||
8258 |
||||
7406 |
||||
3915 |
||||
8382 |
||||
2437 |
||||
2837 |
||||
82 |
||||
6871 |
||||
1876 |
||||
7447 |
||||
8285 |
||||
5007 |
||||
2740 |
||||
3463 |
||||
5103 |
||||
3755 |
||||
4910 |
||||
6809 |
||||
3800 |
||||
118 |
||||
3396 |
||||
3092 |
||||
2709 |
||||
81 |
||||
7105 |
||||
4036 |
||||
2366 |
||||
1846 |
||||
5177 |
||||
2684 |
||||
64 |
||||
2041 |
||||
3919 |
||||
700 |
||||
3724 |
||||
1742 |
||||
39 |
||||
807 |
||||
7184 |
||||
2256 |
||||
235 |
||||
2778 |
||||
2996 |
||||
2030 |
||||
3714 |
||||
7167 |
||||
2369 |
||||
6705 |
||||
6861 |
||||
5096 |
||||
2597 |
||||
2166 |
||||
2036 |
||||
3228 |
||||
3747 |
||||
2711 |
||||
8300 |
||||
2226 |
||||
7153 |
||||
7255 |
||||
2631 |
||||
7109 |
||||
8242 |
||||
7445 |
||||
3776 |
||||
3803 |
||||
3690 |
||||
2025 |
||||
2521 |
||||
2316 |
||||
7190 |
||||
8249 |
||||
3352 |
||||
2639 |
||||
2887 |
||||
100 |
||||
4219 |
||||
3344 |
||||
5008 |
||||
7224 |
||||
3351 |
||||
2434 |
||||
2074 |
||||
2034 |
||||
8304 |
||||
5004 |
||||
6868 |
||||
5102 |
||||
2645 |
||||
4071 |
||||
2716 |
||||
2717 |
||||
7420 |
||||
3499 |
||||
3763 |
||||
5084 |
||||
2676 |
||||
2046 |
||||
5107 |
||||
5097 |
||||
3944 |
||||
4097 |
||||
7132 |
||||
3956 |
||||
7343 |
||||
|
After Width: | Height: | Size: 1.4 MiB |
|
After Width: | Height: | Size: 320 B |
|
After Width: | Height: | Size: 377 B |
|
After Width: | Height: | Size: 451 B |
|
After Width: | Height: | Size: 508 B |
|
After Width: | Height: | Size: 577 B |
|
After Width: | Height: | Size: 631 B |
|
After Width: | Height: | Size: 697 B |
|
After Width: | Height: | Size: 753 B |
|
After Width: | Height: | Size: 321 B |
|
After Width: | Height: | Size: 388 B |
|
After Width: | Height: | Size: 458 B |
|
After Width: | Height: | Size: 514 B |
|
After Width: | Height: | Size: 581 B |
|
After Width: | Height: | Size: 654 B |
|
After Width: | Height: | Size: 726 B |
|
After Width: | Height: | Size: 804 B |
|
After Width: | Height: | Size: 305 B |
|
After Width: | Height: | Size: 340 B |
|
After Width: | Height: | Size: 354 B |
|
After Width: | Height: | Size: 371 B |
|
After Width: | Height: | Size: 398 B |
|
After Width: | Height: | Size: 411 B |
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Reference in new issue