xiangyu8 / pt-map-sf Goto Github PK

.3.bn2.bias", "module.block1.layer.3.bn2.running_mean", "module.block1.layer.3.bn2.running_var", "module.block1.layer.3.bn2.num_batches_tracked", "module.block1.layer.3.conv2.weight", "module.block2.layer.0.bn1.weight", "module.block2.layer.0.bn1.bias", "module.block2.layer.0.bn1.running_mean", "module.block2.layer.0.bn1.running_var", "module.block2.layer.0.bn1.num_batches_tracked", "module.block2.layer.0.conv1.weight", "module.block2.layer.0.bn2.weight", "module.block2.layer.0.bn2.bias", "module.block2.layer.0.bn2.running_mean", "module.block2.layer.0.bn2.running_var", "module.block2.layer.0.bn2.num_batches_tracked", "module.block2.layer.0.conv2.weight", "module.block2.layer.0.convShortcut.weight", "module.block2.layer.1.bn1.weight", "module.block2.layer.1.bn1.bias", "module.block2.layer.1.bn1.running_mean", "module.block2.layer.1.bn1.running_var", "module.block2.layer.1.bn1.num_batches_tracked", "module.block2.layer.1.conv1.weight", "module.block2.layer.1.bn2.weight", "module.block2.layer.1.bn2.bias", "module.block2.layer.1.bn2.running_mean", "module.block2.layer.1.bn2.running_var", "module.block2.layer.1.bn2.num_batches_tracked", "module.block2.layer.1.conv2.weight", "module.block2.layer.2.bn1.weight", "module.block2.layer.2.bn1.bias", "module.block2.layer.2.bn1.running_mean", "module.block2.layer.2.bn1.running_var", "module.block2.layer.2.bn1.num_batches_tracked", "module.block2.layer.2.conv1.weight", "module.block2.layer.2.bn2.weight", "module.block2.layer.2.bn2.bias", "module.block2.layer.2.bn2.running_mean", "module.block2.layer.2.bn2.running_var", "module.block2.layer.2.bn2.num_batches_tracked", "module.block2.layer.2.conv2.weight", "module.block2.layer.3.bn1.weight", "module.block2.layer.3.bn1.bias", "module.block2.layer.3.bn1.running_mean", "module.block2.layer.3.bn1.running_var", "module.block2.layer.3.bn1.num_batches_tracked", "module.block2.layer.3.conv1.weight", "module.block2.layer.3.bn2.weight", "module.block2.layer.3.bn2.bias", "module.block2.layer.3.bn2.running_mean", "module.block2.layer.3.bn2.running_var", "module.block2.layer.3.bn2.num_batches_tracked", "module.block2.layer.3.conv2.weight", "module.block3.layer.0.bn1.weight", "module.block3.layer.0.bn1.bias", "module.block3.layer.0.bn1.running_mean", "module.block3.layer.0.bn1.running_var", "module.block3.layer.0.bn1.num_batches_tracked", "module.block3.layer.0.conv1.weight", "module.block3.layer.0.bn2.weight", "module.block3.layer.0.bn2.bias", "module.block3.layer.0.bn2.running_mean", "module.block3.layer.0.bn2.running_var", "module.block3.layer.0.bn2.num_batches_tracked", "module.block3.layer.0.conv2.weight", "module.block3.layer.0.convShortcut.weight", "module.block3.layer.1.bn1.weight", "module.block3.layer.1.bn1.bias", "module.block3.layer.1.bn1.running_mean", "module.block3.layer.1.bn1.running_var", "module.block3.layer.1.bn1.num_batches_tracked", "module.block3.layer.1.conv1.weight", "module.block3.layer.1.bn2.weight", "module.block3.layer.1.bn2.bias", "module.block3.layer.1.bn2.running_mean", "module.block3.layer.1.bn2.running_var", "module.block3.layer.1.bn2.num_batches_tracked", "module.block3.layer.1.conv2.weight", "module.block3.layer.2.bn1.weight", "module.block3.layer.2.bn1.bias", "module.block3.layer.2.bn1.running_mean", "module.block3.layer.2.bn1.running_var", "module.block3.layer.2.bn1.num_batches_tracked", "module.block3.layer.2.conv1.weight", "module.block3.layer.2.bn2.weight", "module.block3.layer.2.bn2.bias", "module.block3.layer.2.bn2.running_mean", "module.block3.layer.2.bn2.running_var", "module.block3.layer.2.bn2.num_batches_tracked", "module.block3.layer.2.conv2.weight", "module.block3.layer.3.bn1.weight", "module.block3.layer.3.bn1.bias", "module.block3.layer.3.bn1.running_mean", "module.block3.layer.3.bn1.running_var", "module.block3.layer.3.bn1.num_batches_tracked", "module.block3.layer.3.conv1.weight", "module.block3.layer.3.bn2.weight", "module.block3.layer.3.bn2.bias", "module.block3.layer.3.bn2.running_mean", "module.block3.layer.3.bn2.running_var", "module.block3.layer.3.bn2.num_batches_tracked", "module.block3.layer.3.conv2.weight", "module.bn1.weight", "module.bn1.bias", "module.bn1.running_mean", "module.bn1.running_var", "module.bn1.num_batches_tracked", "module.linear.L.weight_g", "module.linear.L.weight_v"

what should be the next step?

您好
我想问一下，backbone训练到什么程度，大概loss到多少的时候，可以停止呢

the attribute folder as well as the parts folder and the bounding boxes.txt,
I have been trying to test your model on my data but it's showing the matrix error RuntimeError: mat1 and mat2 shapes cannot be multiplied (4x128 and 640x5) so if you could help me out with the data preparation would be very grateful

I don't find the realization of formula 2 of the paper ,and what does N mean in the formula ?

Hi, I noticed that both your code and https://github.com/yhu01/PT-MAP.git are called PT-MAP. May I ask if you successfully trained the code in https://github.com/yhu01/PT-MAP.git? Can you think that your code is his improved version?

Hi,

I'm trying to reproduce the results obtained using ResNet-18 or other ResNet networks as the backbone. Would it be possible for you to provide the pretrained model in .plk format?

Thank you in advance.

And would there be a way to run inference on feed from a webcam ?

thanks.

Hello,
Thank you for the pre-trained network you supplied.
I'm trying to reproduce your results, but the checkpoints files miss the .tar file of 'both' for MiniImagenet.

Can you supply this checkpoint file?

The code is complex. Can you explain where is the DCT model?

S2M2_R (s) 80.55 +- 0.78 on CUB, 5-WAY 1-SHOT
By downloading the checkpoint you shared, only 63.3% of the above tasks were obtained. Can you help me analyze the reasons?