PyTorch standard Coco dataset (datasets.CocoDetection) not compatible with Faster R-CNN object detection model

🐛 Describe the bug

Hi,
I am trying to train and evaluate pre-trained Faster R-CNN model with standard coco dataset. I am getting the following error

TypeError: RandomIoUCrop() requires input sample to contain tensor or PIL images and bounding boxes. Sample can also contain masks.

Here are the high level steps

1. Downloaded the COCO 2017 dataset
2. Prepared PyTorch dataset using standard steps from https://pytorch.org/vision/main/auto_examples/transforms/plot_transforms_e2e.html#sphx-glr-auto-examples-transforms-plot-transforms-e2e-py
3. Training and evaluating Faster R-CNN model using steps from https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

Here is colab notebook
https://colab.research.google.com/drive/1Tbu2Thf-thn0lLG12dM3bq_BMZDihX2Y?usp=sharing

Any help will be appreciated. Thanks.

Versions

Hi,
I am trying to train and evaluate pre-trained Faster R-CNN model with standard coco dataset. I am getting the following error

TypeError: RandomIoUCrop() requires input sample to contain tensor or PIL images and bounding boxes. Sample can also contain masks.

Here are the high level steps

1. Downloaded the COCO 2017 dataset
2. Prepared PyTorch dataset using standard steps from https://pytorch.org/vision/main/auto_examples/transforms/plot_transforms_e2e.html#sphx-glr-auto-examples-transforms-plot-transforms-e2e-py
3. Training and evaluating Faster R-CNN model using steps from https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

Here is colab notebook
https://colab.research.google.com/drive/1Tbu2Thf-thn0lLG12dM3bq_BMZDihX2Y?usp=sharing

Any help will be appreciated. Thanks.

Reproduce the failure using the linked Colab notebook and trace the sample passed from datasets.CocoDetection through RandomIoUCrop into Faster R-CNN. Compare the transform example and detection tutorial to identify the expected image and bounding-box sample structure; done means standard COCO preparation runs through training and evaluation without the TypeError.