the repo is forked from [liangeming's github repo]: https://github.com/liangheming/retinanetv1

RetinaNet

This is an unofficial pytorch implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He and Piotr Dollár.

requirement

tqdm
pyyaml
numpy
opencv-python
pycocotools
torch >= 1.6
torchvision >=0.7.0

result

we trained this repo on 4 GPUs with batch size 8 (4 image per node). the total epoch is 24 ,Adam with cosine lr decay is used for optimizing. finally, this repo achieves 34.6 mAp at 896px(max side) resolution with resnet50 backbone.

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.346
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.544
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.362
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.221
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.401
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.455
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.285
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.460
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.491
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.306
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.573
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.620

training

for now we only support coco detection data.

run train scripts

 python -m torch.distributed.launch --nproc_per_node=4 main.py

Edwardwaw/retinanet

RetinaNet

requirement

result

training

tricks