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| | | 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` |
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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 cfg/yolov3-voc.cfg /backup/yolo-voc_1000.weights -gpus 0,1,2,3` |
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| | | 2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-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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| | | 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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| | | * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689 |
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| | | * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776 |
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| | | So if `classes=1` then should be `filters=18`. If `classes=2` then write `filters=31`. |
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| | | So if `classes=1` then should be `filters=18`. If `classes=2` then write `filters=21`. |
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| | | **(Do not write in the cfg-file: filters=(classes + 5)x3)** |
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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 - in the same directory and 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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| | | 5. You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark |
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| | | It will create `.txt`-file for each `.jpg`-image-file - in the same directory and 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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| | | * `<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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| | | * for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>` |
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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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| | | For example for `img1.jpg` you will be created `img1.txt` containing: |
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| | | ``` |
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| | | 1 0.716797 0.395833 0.216406 0.147222 |
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| | | * Also you can get result earlier than all 45000 iterations. |
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| | | **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. |
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| | | ### How to train tiny-yolo (to detect your custom objects): |
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| | | Do all the same steps as for the full yolo model as described above. With the exception of: |
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