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HUST Vision Lab 90

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: 37.6
• Recieved mAP: 33.2

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 ] = 0.332
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.530
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.340
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.115
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.352
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.282
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.411
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.423
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.161
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.454
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.661