| | |
|---|
| | | * 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0` |
|---|
| | | * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` |
|---|
| | | * 186 MB Yolo9000 - video: `darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4` |
|---|
| | | * Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app |
|---|
| | | * To process a list of images `image_list.txt` and save results of detection to `result.txt` use: |
|---|
| | | `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights < image_list.txt > result.txt` |
|---|
| | | You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509 |
|---|
| | |
|---|
| | | |
|---|
| | | 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\` |
|---|
| | | |
|---|
