I would like to use the yolo architecture for object detection. Before training the network with my custom data, I followed these steps to train it on the Pascal VOC data: https://pjreddie.com/darknet/yolo/
The instructions are very clear. But after the final step
./darknet detector train cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23
darknet immediately stops training and announces that weights have been written to the backups/ directory.
At first I thought that the pretraining was simply too good and that the stopping criteria would be reached at once.
So I've used the ./darknet detect command with these weights on one of the test images data/dog. Nothing is found.
If I don't use any pretrained weights, the network does train. I've edited cfg/yolo-voc.cfg to use
# Testing
#batch=1
#subdivisions=1
# Training
batch=32
subdivisions=8
Now the training process has been runnning for many hours and is keeping my gpu warm.
Is this the intended way to train darknet ? How can I use pretrained weights correctly, without training just breaking off ?
Is there any setting to create checkpoints, or get an idea of the progress ?
This is an old question so I hope you have your answer by now, but here is mine just in case it helps.
After working with darknet for about a month, I've run into most of the roadblocks that people have asked/posted about on forums. In your case, I'm pretty certain it's because the weights have been trained for the max number of batches already, and when the pre-trained weights were read in darknet assumed training was done.
Relevant personal experience: when I used one of the pretrained weights files, it started from iteration 40101 and ran until 40200 before cutting off.
I would stick to training from scratch if you have custom data, but if you want to try the pre-trained weights again, you might find that changing max batches in the cfg file helps.