How can I decrese overfitting like Weight Decay ,Early Stopping and Dropout?
This is a training stage issue.
Usually, we don’t apply training on the Jetson platform due to some hardware design limitation.
It’s recommended to apply training on our desktop GPU instead.
To solve the over-fitting issue, you can try GooglenetV2 which introduces a batch normalization layer.
I use this web(https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-collect.md) to train googlenet. Sorry, I am not clear to say this problem.Can I adjust googlenet’s program function on train ? Or does this web(https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-collect.md) have the googlenet’s program to adjust googlenet’s program function on train ? I say program function like this.
Sorry for the late update.
The tutorial is based on pyTorch frameworks so you can set the training parameter as pyTorch manner.
python3 train.py ... --epochs 100 ...