To participate and submit to this HOS challenge, register at the HOS Codalab Challenge.

Please Go to EPIC-KITCHENS VISOR official webpage to download the whole dataset, providing RGB frames, masks and hand-object relations in train/val/test splits. If you are interested in our data generation pipeline, please also check our VISOR paper.

• Refer to VISOR-HOS repository for how to load the data and generate COCO format annotation from the raw format in the download. You can also use other ways to load the data and other data formats too.
• Refer to VISOR-HOS Environment session for environment setup.

• FYI, we use the COCO Mask AP metric implemented in Detectron2 to get the numbers in our paper and in our challenge.
• Plese refer to VISOR-HOS to check how we get COCO Mask AP evaluation in our baseline method.
• For the HOS Codalab Challenge evaluation, with the 4 prediction PTH files prepared as instructed in the Codalab challenge, you can run the commend below to get the scores as in the table.
• For more details about the prediction PTH file and its format, if you use the Detectron2 COCOEvaluator, there will be a instances_predictions.pth file automatically generated and saved in the output folder, /path/to/your/outputs/inference. So anytime you are confused or uncertain about the PTH format, you can check that too.

Get VISOR-HOS as submodule of this repository:

git submodule update --init

Make sure your input_dir folder structure is as below. Under the input_dir, there will be a ref sub-folder containing the COCO format annotations and a res sub-folder containing the predictions of your method in the required format.

/path/to/your/inputs
|--- ref
|    |--- epick_visor_coco_hos
|         |---annotations
|             |---val.json
|    |--- epick_visor_coco_handside
|         |---annotations
|             |---val.json
|    |--- epick_visor_coco_contact
|         |---annotations
|             |---val.json
|    |--- epick_visor_coco_combineHO
|         |---annotations
|             |---val.json
|--- res
|    |--- instances_predictions_hand_obj.pth
|    |--- instances_predictions_handside.pth
|    |--- instances_predictions_contact.pth
|    |--- instances_predictions_combineHO.pth

Then, simply evaluate with the command below:

python evaluate_hos.py --input_dir=/path/to/your/inputs --output_dir=/path/to/your/outputs