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detr-resnet-101

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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: 43.5
• Recieved mAP: 40.9

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 ] = 0.409
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.603
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.439
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.325
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.483
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.496
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.544
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697