Using the model for image classification only tasks about detr HOT 3 CLOSED

model = torch.hub.load('facebookresearch/detr', 'detr_resnet50', pretrained=True)
x = torch.randn((10,3,800,400)) # batch size of 10
model(x)

      2 
      3     # propagate through the model
----> 4 outputs = detr(x)
      5 print(outputs)

3 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

<ipython-input-2-f9392da44538> in forward(self, inputs)
     63         # propagate through the transformer
     64         h = self.transformer(pos + 0.1 * h.flatten(2).permute(2, 0, 1),
---> 65                              self.query_pos.unsqueeze(1)).transpose(0, 1)
     66 
     67         # finally project transformer outputs to class labels and bounding boxes

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/transformer.py in forward(self, src, tgt, src_mask, tgt_mask, memory_mask, src_key_padding_mask, tgt_key_padding_mask, memory_key_padding_mask)
    113 
    114         if src.size(1) != tgt.size(1):
--> 115             raise RuntimeError("the batch number of src and tgt must be equal")
    116 
    117         if src.size(2) != self.d_model or tgt.size(2) != self.d_model:

RuntimeError: the batch number of src and tgt must be equal