[Semantic Segmentation] - Problem when re-training Semantic segmentation, Accuracy decrease with parameter default?

[vanloctc]

Hi @charlesq34,

I'm really grateful when you public this source for me to have a chance to learn. I'm trying to re-training PointNet Semantic Segmentation but seems to be the training process met overfitting/underfitting with the 10th epoch. I still try for researching the cause but I think you can know the more cleary result. This is a summary result:

(log_train.txt) - almost file

EPOCH 009 mean loss: 271829.089959 accuracy: 0.549186

eval mean loss: 167083.881273 eval accuracy: 0.421601 eval avg class acc: 0.260372 EPOCH 010

mean loss: 129570.972106 accuracy: 0.561843

eval mean loss: 103253.728045 eval accuracy: 0.484906 eval avg class acc: 0.297415 Model saved in file: log6/model.ckpt EPOCH 011

mean loss: 37190426.054835 accuracy: 0.354552

eval mean loss: 6327.641183 eval accuracy: 0.462198 eval avg class acc: 0.178751 EPOCH 012

mean loss: 5618.020521 accuracy: 0.337940

eval mean loss: 4386.426989 eval accuracy: 0.203347 eval avg class acc: 0.133981

[vanloctc]

This image above is the plot of loss and accuracy of log_train.txt

[Swaraj-72]

Hello @vanloctc, have you found any feasible solution for your case? I have been facing similar issue with my custom dataset.