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| | | More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc |
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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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| | | 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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| | | 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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| | | * change line `classes=20` to your number of objects |
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| | | * change line `filters=425` 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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| | | |
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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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| | | |
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| | | ``` |
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| | | [convolutional] |
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| | | filters=35 |
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| | | [region] |
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| | | classes=2 |
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| | | ``` |
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| | | |
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| | | 2. Create file `obj.names` in the directory `build\darknet\x64\data\`, with objects names - each in new line |
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| | | 3. Create file `obj.data` in the directory `build\darknet\x64\data\`, containing (where **classes = number of objects**): |
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| | | |
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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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| | | backup = backup/ |
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| | | ``` |
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| | | |
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| | | 4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\` |
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| | | 5. Create `.txt`-file for each `.jpg`-image-file - with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: `<object-class> <x> <y> <width> <height>` |
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| | | Where: |
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| | | * `<object-class>` - integer number of object from `0` to `(classes-1)` |
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| | | * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0 |
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| | | * atention: `<x> <y>` - are center of rectangle (are not top-left corner) |
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| | | For example for `img1.jpg` you should create `img1.txt` containing: |
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| | | ``` |
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| | | 1 0.716797 0.395833 0.216406 0.147222 |
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| | | 0 0.687109 0.379167 0.255469 0.158333 |
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| | | 1 0.420312 0.395833 0.140625 0.166667 |
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| | | ``` |
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| | | 6. Create file `train.txt` in directory `build\darknet\x64\data\`, with filenames of your images, each filename in new line, with path relative to `darknet.exe`, for example containing: |
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| | | ``` |
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| | | data/obj/img1.jpg |
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| | | data/obj/img2.jpg |
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| | | data/obj/img3.jpg |
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| | | ``` |
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| | | 7. Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory `build\darknet\x64` |
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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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| | | ## How to mark bounded boxes of objects and create annotation files: |
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| | | Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2: https://github.com/AlexeyAB/Yolo_mark |
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| | | 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 |
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