Questions about code and dataset

[SherlockHolmes221]

1. PPDM achieves high mAP in HICO and HOI-A dataset which are both big dataset, what about the performance on small dataset like v-coco?
2. What is the effect of the affine_transform when producing labeled heatmap?
   Thanks

[YueLiao]

1. We only evaluate our PPDM based on our rewriting evaluation script for V-COCO. Hourglass-104 achieved about 59.
2. Because we follow Objects as points to apply a data augmentation during training, the affine_transform function is used to generate the corresponding GT heatmap for the transformed input, e.g., scale crop aug, flip etc.

[SherlockHolmes221]

The v-coco mAP is the same with the official one?

[YueLiao]

We do not make sure it. We haven't conducted the evaluation in the official one. Its work is in the to-do list but not now, may next month. You can try it by yourself, and we have provided the corresponding training code for vcoco.

[SherlockHolmes221]

Thanks, closing it

[SherlockHolmes221]

1. We only evaluate our PPDM based on our rewriting evaluation script for V-COCO. Hourglass-104 achieved about 59.
2. Because we follow Objects as points to apply a data augmentation during training, the affine_transform function is used to generate the corresponding GT heatmap for the transformed input, e.g., scale crop aug, flip etc.

Could you share vcoco(Hourglass-104) training scripts or hyperparameters ? I tried but can not reach 59.

[YueLiao]

All hyper-parameters are the same as hico-det's. Additionally, we only conduct an evaluation based on the non-official evolutional script, which cannot be compared to the methods evaluated by the official script.

[SherlockHolmes221]

Thanks

[SherlockHolmes221]

All hyper-parameters are the same as hico-det's. Additionally, we only conduct an evaluation based on the non-official evolutional script, which cannot be compared to the methods evaluated by the official script.

It is weird that I use the same hyper-parameters(lr3e-4 bs 31) in hico-det's training scripts to train vcoco, but still can not reach 59 using your vcoco_eval.py test script. I just get about 54.

[YueLiao]

All hyper-parameters are the same as hico-det's. Additionally, we only conduct an evaluation based on the non-official evolutional script, which cannot be compared to the methods evaluated by the official script.

It is weird that I use the same hyper-parameters(lr3e-4 bs 31) in hico-det's training scripts to train vcoco, but still can not reach 59 using your vcoco_eval.py test script. I just get about 54.

I try to find the checkpoint, but I found all of them are deleted due to the space limitation. The 59 is got by the older version code, and you can try it based on this repo. Hope you can reproduce it.

[YueLiao]

All hyper-parameters are the same as hico-det's. Additionally, we only conduct an evaluation based on the non-official evolutional script, which cannot be compared to the methods evaluated by the official script.

It is weird that I use the same hyper-parameters(lr3e-4 bs 31) in hico-det's training scripts to train vcoco, but still can not reach 59 using your vcoco_eval.py test script. I just get about 54.

I try to find the checkpoint, but I found all of them are deleted due to the space limitation. The 59 is got by the older version code, and you can try it based on this repo. Hope you can reproduce it.

Aha, I have just found the checkpoint, OneDrive or BaiduDrive? The only preserved one. However, I don't think the current training and test strategy is fit for the official vcoco evaluation method. It may be better to give a threshold to remove the non-interaction predictions or to take the non-interaction images into training.

[SherlockHolmes221]

Thanks, both OneDrive and BaiduDrive is ok

[YueLiao]

Thanks, both OneDrive and BaiduDrive is ok

OneDrive.