PyTorch standard Coco dataset (datasets.CocoDetection) not compatible with Faster R-CNN object detection model about vision HOT 7 OPEN

Hi @ranjaniocl ,

try to print the input that gets passed to RandomIoUCrop(). There should be bounging boxes and PILimages/tensors in there. If not, it's likely that the pipeline is incorrect.

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Hi @NicolasHug,

Thank for looking into my issue. I do not know how to print input that get passed to RandomIoUCrop(). Can you please guide me with sample script/steps?
Also, we do you mean when you say 'pipeline'. Is it the dataloader?

I tried to print a sample dataset in the notebook and it has tensors for image and bounding boxes.

from vision.

@ranjaniocl sorry it looks like your issue might be more in scope for https://discuss.pytorch.org/

from vision.

@NicolasHug Ok. sure. I will try my luck there.
As I am using standard dataset and Pytorch provided standard code, I thought someone here can look into it and provide some resolution.

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@NicolasHug Just for reference, there was one similar issue reported in past.
#2720

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@NicolasHug I just logged it at PyTorch forum. While I was creating, similar issues from past popped up (please see links below). I do not see any response so I do not have much hope.

https://discuss.pytorch.org/t/training-faster-r-cnn-model-with-coco-dataset-has-been-consistently-unsuccessful/178023
https://discuss.pytorch.org/t/evaluate-pre-trained-faster-r-cnn-on-coco-dataset/157770

from vision.

Help on class CocoDetection in module torchvision.datasets.coco:

class CocoDetection(torchvision.datasets.vision.VisionDataset)
| CocoDetection(root: Union[str, pathlib.Path], annFile: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None) -> None

Note: the class has transform, target_transform and transforms arguments, passing value to transforms (not transform) can solve the issue for me.

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