FasterRCNN-pytorch:FasterRCNN在VGG，ResNet和FPN基础中实现

# FasterRCNN-pytorch FasterRCNN is implemented in VGG, ResNet and FPN base. reference: rbg's FasterRCNN code: [https://github.com/rbgirshick/py-faster-rcnn](https://github.com/rbgirshick/py-faster-rcnn) ----- # Model Performance ### Train on VOC2017 Test on VOC2017 | Backbone | mAp | | ---------- |:------:| | VGG16 | 0.7061 | | ResNet101 | 0.754 | # Train Your Model ### 1.Before Run You Need: 1. cd ./lib > Change gpu_id in make.sh and setup.py. Detially, you need modify parameter setting in line 5, 12 and 19 in make.sh and line 143 in setup.py where include key words '-arch=' depend on your gpu model.(select appropriate architecture described in table below) > sh make.sh | GPU model | Architecture | | -------- | :-----: | | TitanX (Maxwell/Pascal) | sm_52 | | GTX 960M | sm_50 | | GTX 108 (Ti) |sm_61    | | Grid K520 (AWS g2.2xlarge)   |sm_30     | | Tesla K80 (AWS p2.xlarge)   |sm_37     | 2. cd ../ > mkdir ./data > mkdir ./data/pretrained_model > download pre-trained weights in ./data/pretrained_model 3. run train.py ### 2.How to use? #### **Note: decentralization in preprocesing is based on BGR channels, so you must guarantee your pre-trained model is trained on the same channel set if you use transfer learning** For example: VGG: CUDA_VISIBLE_DEVICES=1 python train.py --net='vgg16' --tag=vgg16 --iters=70000 --cfg='./experiments/cfgs/vgg16.yml' --weight='./data/pretrained_model/vgg16_caffe.pth' CUDA_VISIBLE_DEVICES=2 python test.py --net='vgg16' --tag=vgg16 --model=60000 --cfg='./experiments/cfgs/vgg16.yml' --model_path='voc_2007_trainval/vgg16/vgg16_faster_rcnn' --imdb='voc_2007_test' --comp ResNet: CUDA_VISIBLE_DEVICES=2 python train.py --net='res18' --tag=res18 --iters=70000 --cfg='./experiments/cfgs/res18.yml' --weight='./data/pretrained_model/Resnet18_imagenet.pth' CUDA_VISIBLE_DEVICES=3 python train.py --net='res50' --tag=res50 --iters=70000 --cfg='./experiments/cfgs/res50.yml' --weight='./data/pretrained_model/Resnet50_imagenet.pth' CUDA_VISIBLE_DEVICES=7 python train.py --net='res101' --tag=res101 --iters=80000 --cfg='./experiments/cfgs/res101.yml' --weight='./data/pretrained_model/resnet101_caffe.pth' CUDA_VISIBLE_DEVICES=6 python test.py --net='res101' --tag=res101_1 --cfg='./experiments/cfgs/res101.yml' --model=70000 --model_path='voc_2007_trainval/res101_1' --imdb='voc_2007_test' --comp ----