JihongJu / keras-fcn

A playable implementation of Fully Convolutional Networks with Keras.
MIT License
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Training on VOC2011 #25

Open mat112 opened 7 years ago

mat112 commented 7 years ago

The README suggests that we should be able to train on VOC2011 just by downloading the dataset and running train.py. Is it really the case? My training seems to converge for the first few epochs, but then the val_loss stops to improve early, and diverge from training loss. In fact the best val_loss I can have is around 1.06. Do you have an idea why?

Thank you!

Epoch 14/100 1112/1112 [==============================] - 878s - loss: 1.0603 - acc: 0.7734 - val_loss: 1.1059 - val_acc: 0.7623 Epoch 15/100 1112/1112 [==============================] - 880s - loss: 1.0474 - acc: 0.7751 - val_loss: 1.0961 - val_acc: 0.7652 Epoch 16/100 1112/1112 [==============================] - 869s - loss: 1.0273 - acc: 0.7784 - val_loss: 1.1116 - val_acc: 0.7609 Epoch 17/100 1112/1112 [==============================] - 869s - loss: 1.0228 - acc: 0.7781 - val_loss: 1.1651 - val_acc: 0.7596 Epoch 18/100 1112/1112 [==============================] - 869s - loss: 1.0054 - acc: 0.7812 - val_loss: 1.1100 - val_acc: 0.7643 Epoch 19/100 1112/1112 [==============================] - 869s - loss: 0.9971 - acc: 0.7834 - val_loss: 1.1266 - val_acc: 0.7609 Epoch 20/100 1112/1112 [==============================] - 869s - loss: 0.9881 - acc: 0.7833 - val_loss: 1.1472 - val_acc: 0.7581 [...] Epoch 44/100 1112/1112 [==============================] - 869s - loss: 0.6450 - acc: 0.8553 - val_loss: 1.2859 - val_acc: 0.7561 Epoch 45/100 1112/1112 [==============================] - 868s - loss: 0.6358 - acc: 0.8582 - val_loss: 1.2139 - val_acc: 0.7645 Epoch 46/100 1112/1112 [==============================] - 869s - loss: 0.6012 - acc: 0.8688 - val_loss: 1.3206 - val_acc: 0.7573 Epoch 47/100 1112/1112 [==============================] - 868s - loss: 0.5956 - acc: 0.8704 - val_loss: 1.2663 - val_acc: 0.7626

JihongJu commented 7 years ago

@mat112 Thank you. I had the same problem when I trained and that is why I don't provide a trained model.

To me, this is a typical overfitting issue and it should be solvable by tuning the regularization term. I am currently busy with other things and don't have the time to tune it. Let me know if you cannot find a proper beta for regularization.

Pan-zhaoyu commented 6 years ago

Thank you for your reply. I have solved the above issue. Since I am a beginner. Now I wonder to know how can I test my own picture using the model and the existing weights in this program? I will appreciate it if you could give me some advice or idea. THx!

hwei-hw commented 6 years ago

@Pan-zhaoyu Thank you. I have met the overfitting issue in train the network. And could you tell me how to fix the issue? I will very appreciate it if you could give me some advice. Thanks very much!

kangmengmeng commented 5 years ago

@Pan-zhaoyu @Atomwh @JihongJu How do you slove the overfitting issue? O_O Thanks!