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| | | * **OpenCV 3.4.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.0/opencv-3.4.0-vc14_vc15.exe/download |
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| | | * **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download |
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| | | - OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi` |
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| | | * **GPU with CC >= 2.0** if you use CUDA, or **GPU CC >= 3.0** if you use cuDNN + CUDA: https://en.wikipedia.org/wiki/CUDA#GPUs_supported |
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| | | * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported |
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| | | |
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| | | ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): |
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| | | * `yolov3.cfg` (236 MB COCO **Yolo v3**) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights |
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| | | Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1) |
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| | | * `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`) |
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| | | * `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`) |
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| | | * `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x |
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| | | * `OPENCV=1` to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams |
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| | | * `DEBUG=1` to bould debug version of Yolo |
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| | | * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU |
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| | | 4.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: `C:\opencv_2.4.13\opencv\build\x64\vc14\lib` |
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| | | 5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x: |
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| | | `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;` |
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| | | |
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| | | ### How to compile (custom): |
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| | | https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ |
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| | | |
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| | | ## How to train (to detect your custom objects): |
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| | | Training Yolo v3 |
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| | | (to train old Yolo v2 `yolov2-voc.cfg`, `yolov2-tiny-voc.cfg`, `yolo-voc.cfg`, `yolo-voc.2.0.cfg`, ... [click by the link](https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data)) |
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| | | |
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| | | Training Yolo v3: |
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| | | 1. Create file `yolo-obj.cfg` with the same content as in `yolov3.cfg` (or copy `yolov3.cfg` to `yolo-obj.cfg)` and: |
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| | | 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` |
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| | | * After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` |
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| | | * After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` |
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| | | (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations `if(iterations > 1000)`) |
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| | | * Also you can get result earlier than all 45000 iterations. |
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| | |
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| | | * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision |
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| | | |
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| | | * recalculate anchors for your dataset for `width` and `height` from cfg-file: |
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| | | `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -heigh 416` |
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| | | `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` |
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| | | then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds |
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| | | * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box |
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| | | * 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) |
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| | | |
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| | | * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file |
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