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jwyang / fpn.pytorch

Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection
MIT License
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COCO Results #29

Open haorui-ji opened 5 years ago

haorui-ji commented 5 years ago

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?

gurkirt commented 5 years ago

Any update?

gaoyao123 commented 5 years ago

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

gurkirt commented 5 years ago

Thank, I got mine working, see it at https://github.com/gurkirt/FPN.pytorch1.0 If you are looking for pytorch implementation

gaoyao123 commented 5 years ago

OK,thanks a lot.

CharlesPikachu commented 4 years ago

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