fpn.pytorch
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jwyang
COCO Results
Hi, thanks for your implementation. Have you got the results from COCO yet? And I'm still wondering why its performance is worse than your faster-rcnn implementation?
I have trained this code in COCO2017 dataset.I used resnet101 for training and other parameters were invariant.I got the following results.Just for reference.
ROI Align checkpoint 5 mAP = 0.25 mAP(Person)=0.380
AP IOU area maxDets AP 0.50:0.95 all 100 0.250 0.50 all 100 0.445 0.75 all 100 0.258 0.50:0.95 small 100 0.115 0.50:0.95 medium 100 0.282 0.50:0.95 large 100 0.344
AR IOU area maxDets AP 0.50:0.95 all 1 0.246 0.50:0.95 all 10 0.367 0.50:0.95 all 100 0.374 0.50:0.95 small 100 0.206 0.50:0.95 medium 100 0.406 0.50:0.95 large 100 0.521
Thank, I got mine working, see it at https://github.com/gurkirt/FPN.pytorch1.0 If you are looking for pytorch implementation
Here is my result of resnet101 epoch 12 after updating this repo to torch1.x and borrow some codes from mmdet:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.587 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.406 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 ] = 0.417 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.313 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.493 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.319 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.664
code is here: https://github.com/DetectionBLWX/FPN.pytorch I'm still working on this repo to obtain more reasonable results
