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| | | More details: http://pjreddie.com/darknet/yolo/ |
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| | | ##### Requires: |
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| | | * **MS Visual Studio 2015 (v140)**: https://www.microsoft.com/download/details.aspx?id=48146 |
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| | | * **MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) |
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| | | * **CUDA 8.0 for Windows x64**: https://developer.nvidia.com/cuda-downloads |
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| | | * **OpenCV 2.4.9**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download |
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| | | - To compile without OpenCV - remove define OPENCV from: Visual Studio->Project->Properties->C/C++->Preprocessor |
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| | | 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` |
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| | | 6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) |
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| | | 6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.2.0.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) |
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| | | 7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` |
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| | | 7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` |
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| | | If required change pathes in the file `build\darknet\x64\data\voc.data` |
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| | | ## How to train with multi-GPU: |
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| | | 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` |
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| | | 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` |
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| | | 2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.cfg yolo-voc_1000.weights -gpus 0,1,2,3` |
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| | | 2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg yolo-voc_1000.weights -gpus 0,1,2,3` |
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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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| | | 1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.cfg` (or copy `yolo-voc.cfg` to `yolo-obj.cfg)` and: |
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| | | 1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.2.0.cfg` (or copy `yolo-voc.2.0.cfg` to `yolo-obj.cfg)` and: |
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| | | * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) |
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| | | * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L4) |
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| | | * change line `classes=20` to your number of objects |
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| | | * change line #224 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L224) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
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| | | * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L237) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
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| | | For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines: |
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| | | For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines: |
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| | | ``` |
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| | | [convolutional] |
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| | | ``` |
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| | | classes= 2 |
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| | | train = train.txt |
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| | | valid = test.txt |
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| | | names = obj.names |
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| | | train = data/train.txt |
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| | | valid = data/test.txt |
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| | | names = data/obj.names |
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| | | backup = backup/ |
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| | | ``` |
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| | | 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` |
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| | | (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations until 1000 iterations has been reached, and after for each 1000 iterations) |
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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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| | | * **9002** - iteration number (number of batch) |
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| | | * **0.060730 avg** - average loss (error) - **the lower, the better** |
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| | | When you see that average loss **0.060730 avg** enough low at many iterations and no longer decreases then you should stop training. |
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| | | When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. |
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| | | 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: |
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| | | 2.1. At first, you should put filenames of validation images to file `data\voc.2007.test` (format as in `train.txt`) or if you haven't validation images - simply copy `data\train.txt` to `data\voc.2007.test`. |
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| | | To get weights from Early Stopping Point: |
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| | | 2.1. At first, in your file `obj.data` you must specify the path to the validation dataset `valid = valid.txt` (format of `valid.txt` as in `train.txt`), and if you haven't validation images, just copy `data\train.txt` to `data\valid.txt`. |
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| | | 2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands: |
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| | | > 7586 7612 7689 RPs/Img: 68.23 **IOU: 77.86%** Recall:99.00% |
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| | | * **IOU** - the bigger, the better (says about accuracy) - **better to use** |
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| | | * **Recall** - the bigger, the better (says about accuracy) |
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| | | * **Recall** - the bigger, the better (says about accuracy) - actually Yolo calculates true positives, so it shouldn't be used |
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| | | For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. |
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| | | For example, **bigger IOU** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. |
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| | | 1. Before training: |
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| | | * set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L244) |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides |
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| | | 2. After training - for detection: |
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