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detr-resnet-50-dc5

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I tried to reproduce the results mentioned on this model card. Seems like my results do not match the claimed mAP in the model card. I cannot figure out how to get the correct numbers, can you help me find my mistake?

• Claimed mAP: 43.3
• Recieved mAP: 41.2

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 ] = 0.412
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.601
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.436
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.451
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.588
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.328
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.494
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.508
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.268
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.554
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.702