| | |
|---|
| | | * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2) |
|---|
| | | * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L3) |
|---|
| | | * change line `classes=20` to your number of objects |
|---|
| | | * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L224) to: filters=(classes + 5)*5, so if `classes=2` then should be `filter=35` |
|---|
| | | * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L224) to: filters=(classes + 5)x5, so if `classes=2` then should be `filters=35`. Or if you use `classes=1` then write `filters=30`, **do not write in the cfg-file: filters=(classes + 5)x5**. |
|---|
| | | |
|---|
| | | (Generally `filters` depends on the `classes`, `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
|---|
| | | (Generally `filters` depends on the `classes`, `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`, where `num` is number of anchors) |
|---|
| | | |
|---|
| | | So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines: |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` |
|---|
| | | |
|---|
| | | (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) |
|---|
| | | (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) |
|---|
| | | |
|---|
| | | 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` |
|---|
| | | |
|---|
