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All Projects → yingxingde → FasterRCNN-pytorch

yingxingde / FasterRCNN-pytorch

Licence: MIT license
FasterRCNN is implemented in VGG, ResNet and FPN base.

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FasterRCNN-pytorch

FasterRCNN is implemented in VGG, ResNet and FPN base.

reference:

rbg's FasterRCNN code: 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

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