Custom Dataset Annotation format

[JJLimmm]

Hi,

I have a few questions after reading the tips and issues pointed out here. I am currently using Labelme to annotate the keypoints and would convert them to the COCO format later on.

1. What if my custom dataset only has keypoints annotated?

2. If i have the 18 keypoints like shown in the image below, where can i change the keypoint definitions in this repo to match my usage?
   

3. And for Validation of the model, are other metrics available like PCK, PCP (more info)? Would i be able to use your validation metrics with just keypoints in my GTs?

Thanks!

Originally posted by @JJLimmm in https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch/issues/231#issuecomment-1207600173

[Daniil-Osokin]

Hi! It is ok, you can convert this format to COCO annotation format. Just throw away the neck and reorder keypoints as in COCO dataset, you can check #134 or the official COCO site for the COCO keypoints order. You can check additional metric in the neighborhood repository. It has the code for a single-person pose estimation.

[JJLimmm]

Thanks @Daniil-Osokin . For my first pointer i think you missed that out by accident, but is it okay if i only have keypoints for my annotations? (no bboxes or segmentations)

[Daniil-Osokin]

To use the code without modifications, you need to convert the data to COCO format. You can take just bounding box around person keypoints. Segmentation, masks are not necessary, check the #231.

[JJLimmm]

I see, so i can do without segmentation masks, but i would still require bounding boxes with my keypoints, so as to use it with COCO's evaluation metrics.

[Daniil-Osokin]

Great, that it is clear now!