| | |
|---|
| | | |
|---|
| | | 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` |
|---|
| | | |
|---|
| | | 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` |
|---|
| | | 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` |
|---|
| | | |
|---|
| | | https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | * Also you can get result earlier than all 45000 iterations. |
|---|
| | | |
|---|
| | | **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. |
|---|
| | | |
|---|
| | | ### How to train tiny-yolo (to detect your custom objects): |
|---|
| | | |
|---|
| | | Do all the same steps as for the full yolo model as described above. With the exception of: |
|---|
