FreeAnchor_retinanet
we trained this repo on 4 GPUs with batch size 64(8 image per node).the total epoch is 20, Adam with cosine lr decay is used for optimizing. finally, this repo achieves 38.5 mAp with resnet50 backbone.
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.385
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.578
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.411
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.430
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.520
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.319
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.508
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.548
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.328
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.598
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713