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Everything seems good but you are not taking any outputs from model1, model2 and model_star? Here is how I would code this thing - import torch import torch.nn as nn import torch.nn.functional as F class VGGBlock(nn.Module): def __init__(self, in_channels, out_channels,batch_norm=False): super(VGGBlock,self).__init__() conv2_params = {'...


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The problem is dealing with multi-class classification. So, in output layer try of using "SoftMax" as the Activation layer.


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You are almost there. You just need to perform the batch inference correctly. So, while model inference you need to convert the list of images to a single tensor, as follows - for i, data in enumerate(dataloader_all, 0): inputs = torch.stack(data['image']) outputs = model_vgg16(inputs) Your code might be creating a tensor of list of images, while ...


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As far as I know, VVGish is the VGG adapted to audio processing. I can remember using it with mfcc, not Amp-Time input tho.


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Your problem is that your dataset has one value per pixel, whereas ImageNet expects 3? Just convert your data to "color images" by passing the same value on all 3 (RGB) channels.


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