| | |
|---|
| | | |
|---|
| | | ## How to train (to detect your custom objects): |
|---|
| | | |
|---|
| | | 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: |
|---|
| | | 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: |
|---|
| | | |
|---|
| | | * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) |
|---|
| | | * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L4) |
|---|
| | |
|---|
| | | |
|---|
| | | ``` |
|---|
| | | classes= 2 |
|---|
| | | train = train.txt |
|---|
| | | valid = test.txt |
|---|
| | | names = obj.names |
|---|
| | | train = data/train.txt |
|---|
| | | valid = data/test.txt |
|---|
| | | names = data/obj.names |
|---|
| | | backup = backup/ |
|---|
| | | ``` |
|---|
| | | |
|---|
| | |
|---|
| | | * **9002** - iteration number (number of batch) |
|---|
| | | * **0.060730 avg** - average loss (error) - **the lower, the better** |
|---|
| | | |
|---|
| | | When you see that average loss **0.060730 avg** enough low at many iterations and no longer decreases then you should stop training. |
|---|
| | | When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. |
|---|
| | | |
|---|
| | | 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: |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | |  |
|---|
| | | |
|---|
| | | 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`. |
|---|
| | | 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`. |
|---|
| | | |
|---|
| | | 2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands: |
|---|
| | | |
|---|
| | |
|---|
| | | > 7586 7612 7689 RPs/Img: 68.23 **IOU: 77.86%** Recall:99.00% |
|---|
| | | |
|---|
| | | * **IOU** - the bigger, the better (says about accuracy) - **better to use** |
|---|
| | | * **Recall** - the bigger, the better (says about accuracy) |
|---|
| | | * **Recall** - the bigger, the better (says about accuracy) - actually Yolo calculates true positives, so it shouldn't be used |
|---|
| | | |
|---|
| | | For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. |
|---|
| | | |
|---|
