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I used the project to train on coco train 2014. And I got the evaluation:
loading annotations into memory... Done (t=4.37s) creating index... index created! 40504 40504 Loading and preparing results... DONE (t=11.58s) creating index... index created! Running per image evaluation... Evaluate annotation type keypoints DONE (t=147.12s). Accumulating evaluation results... DONE (t=1.72s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.229 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.614 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.116 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.191 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.281 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.310 Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.686 Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.241 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.259 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.378
How do I train to mAP(kp, 50)=80+ on maskrcnn paper?
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I used the project to train on coco train 2014. And I got the evaluation:
loading annotations into memory...
Done (t=4.37s)
creating index...
index created!
40504
40504
Loading and preparing results...
DONE (t=11.58s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type keypoints
DONE (t=147.12s).
Accumulating evaluation results...
DONE (t=1.72s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.229
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.614
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.116
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.191
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.281
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.310
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.686
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.241
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.259
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.378
How do I train to mAP(kp, 50)=80+ on maskrcnn paper?
The text was updated successfully, but these errors were encountered: