The input to the model is expected to be a list of tensors, each of shape [C, H, W],
one for each image, and should be in 0-1 range. Different images can have different sizes.

The behavior of the model changes depending if it is in training or evaluation mode.

During training, the model expects both the input tensors, as well as a targets (list of dictionary), containing:

boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format,
with values between 0 and H and 0 and W

labels (Int64Tensor[N]): the class label for each ground-truth box

The model returns a Dict[Tensor] during training, containing the classification and regression losses for both the RPN and the R-CNN.

During inference, the model requires only the input tensors, and returns the post-processed predictions as a List[Dict[Tensor]],
one for each input image. The fields of the Dict are as follows:

boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between 0 and H and 0 and W

labels (Int64Tensor[N]): the predicted labels for each image

scores (Tensor[N]): the scores or each prediction

NEED FINETUNE