Comments (7)
@Neo96Mav , which network you used, or you have modified the network yourself based my code?
from residualattentionnetwork-pytorch.
I have used your network and the official Caffe network for reference, and implemented my own small network. I am not using attention modules for 4x4 because I feel they are too small, and I am only using one attention module in 8x8. My network is relatively small, and its for CIFAR images only.
Can you let me know the intuition behind this -
You have added output of residual block, as well as the output of the skip connection to the upsampled layer!
from residualattentionnetwork-pytorch.
@Neo96Mav , this is refer to the caffe network, i think it is added for more detail information. You can remove it for testing the effectiveness.
from residualattentionnetwork-pytorch.
Hi @Neo96Mav,
Did you test the model using only one 8x8 Attention module? Was the accuracy better?
from residualattentionnetwork-pytorch.
Hi @josianerodrigues , I added the 4x4 attention module as well. I am stuck at 89.5% accuracy. Maybe my model is not big enough or I am not using the exact same configuration, but I feel that it should not have affected it so much. @tengshaofeng Do u have any ideas why we can't match the authors performance?
from residualattentionnetwork-pytorch.
@Neo96Mav , the paper only give the archietcture details of attention_92 for imagenet with 224 input but not for cifar10. So I build the net ResidualAttentionModel_92_32input following my understanding.
I have tested on it on cifar10 test set, the result is as following:
Accuracy of the model on the test images: 0.9354
maybe some details is not good. you can refer to the data preprocessed in the paper, keep same with the author. or maybe you can tune the hyper parameters for better performance. U can also remove the add operation to test the network.
from residualattentionnetwork-pytorch.
@Neo96Mav @josianerodrigues
the result now is 0.954
from residualattentionnetwork-pytorch.
Related Issues (20)
- Traceback (most recent call last): File "train.py", line 20, in <module> from model.residual_attention_network import ResidualAttentionModel_92_32input_update as ResidualAttentionModel ImportError: No module named model.residual_attention_network
- Expression of mix attention HOT 2
- about the code "out_interp = self.interpolation1(out_middle_2r_blocks) + out_down_residual_blocks1" HOT 4
- Focus of the attention mask
- Error : Data must be sequence , got float
- The error about if __name__ == '__main__': freeze_support() HOT 1
- model = ResidualAttentionModel() error with python3 HOT 1
- Hi,is there any impletation of visualizing the mask? i'm insterest in the mask they showed in the paper,it seems very good HOT 1
- A Inputsize Question HOT 1
- What is the meaning of `softmax` in attention_module.py?
- During the test, cifar10, the output data structure is incorrect. HOT 1
- have you ever tested the num of theparams
- i think the num of params for cifar10 residual network is incorrect
- what's the version of torch, torchvision and python? HOT 1
- Questions about the performance on ImageNet
- Errors when I run train.py
- Errors when I run train.py HOT 2
- transfer learning
- stage 0
- Errors when I try to run train.py HOT 2
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from residualattentionnetwork-pytorch.