Merge branch 'dev/alexey/master' into dev/cenit/setup

.github/FUNDING.yml

@@ -0,0 +1,12 @@
+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+custom: ['https://paypal.me/alexeyab84', 'https://blockchain.coinmarketcap.com/address/bitcoin/36La9T7DoLVMrUQzm6rBDGsxutyvDzbHnp', 'https://etherscan.io/address/0x193d56BE3C65e3Fb8f48c291B17C0702e211A588#', 'https://explorer.zcha.in/accounts/t1PzwJ28Prb7Nk8fgfT3RXCr6Xtw54tgjoy'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

Makefile

@@ -118,7 +118,7 @@ LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed
 #-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 
 endif
 
-OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.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 darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o
+OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.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 darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o
 ifeq ($(GPU), 1) 
 LDFLAGS+= -lstdc++ 
 OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
@@ -127,12 +127,12 @@ endif
 OBJS = $(addprefix $(OBJDIR), $(OBJ))
 DEPS = $(wildcard src/*.h) Makefile include/darknet.h
 
-all: obj backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)
+all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)
 
 ifeq ($(LIBSO), 1)
 CFLAGS+= -fPIC
 
-$(LIBNAMESO): $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp
+$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp
 	$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)
 
 $(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp
@@ -151,8 +151,8 @@ $(OBJDIR)%.o: %.cpp $(DEPS)
 $(OBJDIR)%.o: %.cu $(DEPS)
 	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
 
-obj:
-	mkdir -p obj
+$(OBJDIR):
+	mkdir -p $(OBJDIR)
 backup:
 	mkdir -p backup
 results:

README.md

@@ -19,8 +19,11 @@ More details: http://pjreddie.com/darknet/yolo/
 
 0.  [Improvements in this repository](#improvements-in-this-repository)
 1.  [How to use](#how-to-use-on-the-command-line)
-2.  [How to compile on Linux](#how-to-compile-on-linux)
+2.  How to compile on Linux
+    * [Using cmake](#how-to-compile-on-linux-using-cmake)
+    * [Using make](#how-to-compile-on-linux-using-make)
 3.  How to compile on Windows
+    * [Using CMake-GUI](#how-to-compile-on-windows-using-cmake-gui)
     * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg)
     * [Legacy way](#how-to-compile-on-windows-legacy-way)
 4.  [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data)
@@ -51,10 +54,6 @@ More details: http://pjreddie.com/darknet/yolo/
 * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
 * on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community
 
-Compiling on **Windows** by using `Cmake-GUI` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution
-
-Compiling on **Linux** by using command `make` (or alternative way by using command: `cmake . && make` )
-
 #### Pre-trained models
 
 There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy:
@@ -63,12 +62,18 @@ There are weights-file for different cfg-files (smaller size -> faster speed & l
 * `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights
 * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights
 * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM:  https://pjreddie.com/media/files/yolov3-tiny.weights
+* `enet-coco.cfg` (EfficientNetb0-Yolo- 45.5% [email protected] - 3.7 BFlops) [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view) and `yolov3-tiny-prn.cfg` (33.1% [email protected] - 3.5 BFlops - [more](https://github.com/WongKinYiu/PartialResidualNetworks))
+
+<details><summary><b>CLICK ME</b> - Yolo v2 models</summary>
+
 * `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights
 * `yolo-voc.cfg` (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
 * `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights
 * `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights
 * `yolo9000.cfg` (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights
 
+</details>
+
 Put it near compiled: darknet.exe
 
 You can get cfg-files by path: `darknet/cfg/`
@@ -165,7 +170,23 @@ On Linux find executable file `./darknet` in the root directory, while on Window
 
 * Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
 
-### How to compile on Linux
+### How to compile on Linux (using `cmake`)
+
+The `CMakeLists.txt` will attempt to find installed optional dependencies like
+CUDA, cudnn, ZED and build against those. It will also create a shared object
+library file to use `darknet` for code development.
+
+Do inside the cloned repository:
+
+```
+mkdir build-release
+cd build-release
+cmake ..
+make
+make install
+```
+
+### How to compile on Linux (using `make`)
 
 Just do `make` in the darknet directory.
 Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1)
@@ -183,9 +204,28 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c
 
 To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights`
 
+### How to compile on Windows (using `CMake-GUI`)
+
+This is the recommended approach to build Darknet on Windows if you have already
+installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, and
+OpenCV > 2.4.
+
+Use `CMake-GUI` as shown here on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png):
+
+1. Configure
+2. Optional platform for generator (Set: x64)
+3. Finish
+4. Generate
+5. Open Project
+6. Set: x64 & Release
+7. Build
+8. Build solution
+
 ### How to compile on Windows (using `vcpkg`)
 
-If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, OpenCV > 2.4, then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution
+If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0,
+cuDNN > 7.0, OpenCV > 2.4, then to compile Darknet it is recommended to use
+[CMake-GUI](#how-to-compile-on-windows-using-cmake-gui).
 
 Otherwise, follow these steps:
 
@@ -327,7 +367,7 @@ Training Yolo v3:
 
   * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3)
   * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4)
-  * change line max_batches to (`classes*2000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes
+  * change line max_batches to (`classes*2000` but not less than `4000`), f.e. [`max_batches=6000`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L20) if you train for 3 classes
   * change line steps to 80% and 90% of max_batches, f.e. [`steps=4800,5400`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L22)
   * change line `classes=80` to your number of objects in each of 3 `[yolo]`-layers:
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610
@@ -445,11 +485,11 @@ Usually sufficient 2000 iterations for each class(object), but not less than 400
   > Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000,  count: 8
   > Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000,  count: 8
   >
-  > **9002**: 0.211667, **0.060730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images
+  > **9002**: 0.211667, **0.60730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images
   > Loaded: 0.000000 seconds
 
   * **9002** - iteration number (number of batch)
-  * **0.060730 avg** - average loss (error) - **the lower, the better**
+  * **0.60730 avg** - average loss (error) - **the lower, the better**
 
   When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. The final avgerage loss can be from `0.05` (for a small model and easy dataset) to `3.0` (for a big model and a difficult dataset).
 
@@ -530,10 +570,14 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
 
   * check that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark
 
+  * my Loss is very high and mAP is very low, is training wrong? Run training with ` -show_imgs` flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files `aug_...jpg`)? If no - your training dataset is wrong.
+
   * for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more
 
   * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) - use as many images of negative samples as there are images with objects
 
+  * What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected.
+
   * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in cfg-file) 
 
   * for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720
@@ -563,7 +607,9 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
 
   * each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used.
 
-  * recalculate anchors for your dataset for `width` and `height` from cfg-file:
+  * to make the detected bounded boxes more accurate, you can add 3 parameters `ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou` to each `[yolo]` layer and train, it will increase [email protected], but decrease [email protected].
+
+  * Only if you are an **expert** in neural detection networks - recalculate anchors for your dataset for `width` and `height` from cfg-file:
   `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
    then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file. But you should change indexes of anchors `masks=` for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. Also you should change the `filters=(classes + 5)*<number of mask>` before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors.
 
@@ -581,6 +627,14 @@ Here you can find repository with GUI-software for marking bounded boxes of obje
 
 With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 & v3
 
+Different tools for marking objects in images:
+1. in C++: https://github.com/AlexeyAB/Yolo_mark 
+2. in Python: https://github.com/tzutalin/labelImg
+3. in Python: https://github.com/Cartucho/OpenLabeling
+4. in C++: https://www.ccoderun.ca/darkmark/
+5. in JavaScript: https://github.com/opencv/cvat
+
+
 ## Using Yolo9000
 
  Simultaneous detection and classification of 9000 objects: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights data/dog.jpg`
@@ -605,8 +659,14 @@ With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`
 
 ## How to use Yolo as DLL and SO libraries
 
-* on Linux - set `LIBSO=1` in the `Makefile` and do `make`
-* on Windows - compile `build\darknet\yolo_cpp_dll.sln` or `build\darknet\yolo_cpp_dll_no_gpu.sln` solution
+* on Linux
+    * using `build.sh` or
+    * build `darknet` using `cmake` or
+    * set `LIBSO=1` in the `Makefile` and do `make`
+* on Windows
+    * using `build.ps1` or
+    * build `darknet` using `cmake` or
+    * compile `build\darknet\yolo_cpp_dll.sln` solution or `build\darknet\yolo_cpp_dll_no_gpu.sln` solution
 
 There are 2 APIs:
 * C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h

build/darknet/darknet.vcxproj

@@ -199,6 +199,7 @@
     <ClCompile Include="..\..\src\detector.c" />
     <ClCompile Include="..\..\src\dice.c" />
     <ClCompile Include="..\..\src\dropout_layer.c" />
+    <ClCompile Include="..\..\src\gaussian_yolo_layer.c" />
     <ClCompile Include="..\..\src\gemm.c" />
     <ClCompile Include="..\..\src\getopt.c" />
     <ClCompile Include="..\..\src\gettimeofday.c" />
@@ -263,6 +264,7 @@
     <ClInclude Include="..\..\src\demo.h" />
     <ClInclude Include="..\..\src\detection_layer.h" />
     <ClInclude Include="..\..\src\dropout_layer.h" />
+    <ClInclude Include="..\..\src\gaussian_yolo_layer.h" />
     <ClInclude Include="..\..\src\gemm.h" />
     <ClInclude Include="..\..\src\getopt.h" />
     <ClInclude Include="..\..\src\gettimeofday.h" />

build/darknet/darknet_no_gpu.vcxproj

@@ -203,6 +203,7 @@
     <ClCompile Include="..\..\src\detector.c" />
     <ClCompile Include="..\..\src\dice.c" />
     <ClCompile Include="..\..\src\dropout_layer.c" />
+    <ClCompile Include="..\..\src\gaussian_yolo_layer.c" />
     <ClCompile Include="..\..\src\gemm.c" />
     <ClCompile Include="..\..\src\getopt.c" />
     <ClCompile Include="..\..\src\gettimeofday.c" />
@@ -267,6 +268,7 @@
     <ClInclude Include="..\..\src\demo.h" />
     <ClInclude Include="..\..\src\detection_layer.h" />
     <ClInclude Include="..\..\src\dropout_layer.h" />
+    <ClInclude Include="..\..\src\gaussian_yolo_layer.h" />
     <ClInclude Include="..\..\src\gemm.h" />
     <ClInclude Include="..\..\src\getopt.h" />
     <ClInclude Include="..\..\src\gettimeofday.h" />