Open 1292765944 opened 8 years ago
You can try it. You can plot loss graph to decide when should stop training. I often stop training when loss does not decreased after 10000 iteration. If your training has loss is not stably decreased, something was wrong. You should check your model, data...
You can change the parameters(e.g. max iterations, learning rate) in $CAFFE_ROOT/examples/ssd/ssd_pascal.py. It's in the solver_params dict, if you're still looking for it.
I notice that in the solver.prototxt the max_iter is only 60000, How do you select the iterations. I notice that when I train the model, the loss is not stably decreased, So when should we stop training? Does more iterations improve detection accuracy? Thank you!