mAP drop
#1
by
mhyatt000
- opened
I tried to reproduce the results mentioned on this model card. The received mAP does not match the claimed mAP in the model card.
- Claimed mAP: 28.7
- Recieved mAP: 24.7
Here are the details for my validation:
- I instantiate pre-trained model with
transformers.pipeline()
and use COCO API to calculate AP from detection bboxes. - Evaluation was performed on macOS CPU.
- Dataset was downloaded from cocodataset.org
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.247
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.427
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.243
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.245
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.231
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.333
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.344
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.563