2018广东工业智造大数据创新大赛——智能算法赛.zip

Feature Pyramid Network on caffe This is the unoffical version Feature Pyramid Network for Feature Pyramid Networks for Object Detection https://arxiv.org/abs/1612.03144 # results `FPN(resnet50)-end2end result is implemented without OHEM and train with pascal voc 2007 + 2012 test on 2007` merged rcnn |mAP@0.5|aeroplane|bicycle|bird|boat|bottle|bus|car|cat|chair|cow| |:--:|:-------:| -----:| --:| --:|-----:|--:|--:|--:|----:|--:| |0.788|0.8079| 0.8036| 0.8010| 0.7293|0.6743|0.8680|0.8766|0.8967|0.6122|0.8646| |diningtable|dog |horse|motorbike|person |pottedplant|sheep|sofa|train|tv| |----------:|:--:|:---:| -------:| -----:| -------:|----:|---:|----:|--:| |0.7330|0.8855|0.8760| 0.8063| 0.7999| 0.5138|0.7905|0.7755|0.8637|0.7736| shared rcnn |mAP@0.5|aeroplane|bicycle|bird|boat|bottle|bus|car|cat|chair|cow| |:--:|:-------:| -----:| --:| --:|-----:|--:|--:|--:|----:|--:| |0.7833|0.8585| 0.8001| 0.7970| 0.7174|0.6522|0.8668|0.8768|0.8929|0.5842|0.8658| |diningtable|dog |horse|motorbike|person |pottedplant|sheep|sofa|train|tv| |----------:|:--:|:---:| -------:| -----:| -------:|----:|---:|----:|--:| |0.7022|0.8891|0.8680| 0.7991| 0.7944| 0.5065|0.7896|0.7707|0.8697|0.7653| # framework megred rcnn framework Network overview: [link](http://ethereon.github.io/netscope/#/gist/c5334efdd667ce41d540e3697de2936c)  shared rcnn Network overview: [link](http://ethereon.github.io/netscope/#/gist/63c0281751afd1b2d50f4c2764b31a4e)  `the red and yellow are shared params` # about the anchor size setting In the paper the anchor setting is `Ratios： [0.5,1,2],scales :[8,]` With the setting and P2~P6, all anchor sizes are `[32,64,128,512,1024]`,but this setting is suit for COCO dataset which has so many small targets. But the voc dataset targets are range `[128,256,512]`. So, we desgin the anchor setting:`Ratios： [0.5,1,2],scales :[8,16]`, this is very import for voc dataset. # usage download voc07,12 dataset `ResNet50.caffemodel` and rename to `ResNet50.v2.caffemodel` ```bash cp ResNet50.v2.caffemodel data/pretrained_model/ ``` - OneDrive download: [link](https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777) `In my expriments, the codes require ~10G GPU memory in training and ~6G in testing. your can design the suit image size, mimbatch size and rcnn batch size for your GPUS.` ### compile caffe & lib ```bash cd caffe-fpn mkdir build cd build cmake .. make -j16 all cd lib make ``` ### train & test shared rcnn ```bash ./experiments/scripts/FP_Net_end2end.sh 1 FPN pascal_voc ./test.sh 1 FPN pascal_voc ``` megred rcnn ```bash ./experiments/scripts/FP_Net_end2end_merge_rcnn.sh 0 FPN pascal_voc ./test_mergercnn.sh 0 FPN pascal_voc ``` 0 1 is GPU id. ### TODO List - [x] all tests passed - [x] evaluate object detection performance on voc - [x] evaluate merged rcnn version performance on voc ### feature pyramid networks for object detection Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2016). Feature pyramid networks for object detection. arXiv preprint arXiv:1612.03144.