test stage mAP(box)=0, mAP(seg)=0.71

[gaoCleo]

Hi, I just download your code and your pretrained paramters. I test the model in the sub-dataset of coco dataset. But I find that the results of bbox's mAP = 0. I have checked that there are no problems with the dataset.

here is the result:

Evaluating bbox... Loading and preparing results... DONE (t=1.75s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=35.44s). Accumulating evaluation results... DONE (t=12.73s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.026 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.005 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.002 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.032 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.049 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.049 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.049 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.026 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.135

Evaluating segm... Loading and preparing results... UserWarning: The key "bbox" is deleted for more accurate mask AP of small/medium/large instances since v2.12.0. This does not change the overall mAP calculation. warnings.warn( DONE (t=4.94s) creating index... index created! Running per image evaluation... Evaluate annotation type segm DONE (t=40.55s). Accumulating evaluation results... DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) DONE (t=13.33s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.716 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.502 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.304 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.513 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.694 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.619 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.480 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.672 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.802 OrderedDict([('bbox_mAP', 0.009), ('bbox_mAP_50', 0.026), ('bbox_mAP_75', 0.005), ('bbox_mAP_s', 0.0), ('bbox_mAP_m', 0.002), ('bbox_mAP_l', 0.032), ('bbox_mAP_copypaste', '0.009 0.026 0.005 0.000 0.002 0.032'), ('segm_mAP', 0.465), ('segm_mAP_50', 0.716), ('segm_mAP_75', 0.502), ('segm_mAP_s', 0.304), ('segm_mAP_m', 0.513), ('segm_mAP_l', 0.694), ('segm_mAP_copypaste', '0.465 0.716 0.502 0.304 0.513 0.694')])