Awesome
da-faster-rcnn-PyTorch
A PyTorch implementation of 'Domain Adaptive Faster R-CNN for Object Detection in the Wild ’
Preparation
Requirements: Python=3.6 and Pytorch=0.4.0
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Install Pytorch
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Our code is conducted based on faster-rcnn.pytorch,please setup the framework by it.
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Download dataset
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we use cityscape and cityscapes-foggy datasets respectly as source and target,the cityscapes dataset could be download Here
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the format of datasets is similar with VOC,you just need to split train.txt to train_s.txt and train_t.txt
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you can also download the dataset GoogleDrive
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Train and Test
1.train the model,you need to download the pretrained model [vgg_caffe](https://github.com/jwyang/faster-rcnn.pytorch) which is different with pure pytorch pretrained model
2.change the dataset root path in ./lib/model/utils/config.py and some dataset dir path in ./lib/datasets/cityscape.py,the default data path is ./data
3 Train the model
# train cityscapes -> cityscapes-foggy
CUDA_VISIBLE_DEVICES=GPU_ID python da_trainval_net.py --dataset cityscape --net vgg16 --bs 1 --lr 2e-3 --lr_decay_step 6 --cuda
# Test model in target domain
CUDA_VISIBLE_DEVICES=GPU_ID python eval/test.py --dataset cityscape --part test_t --model_dir=# The path of your pth model --cuda
Our model could arrive mAP=30.71% in target domain which is high than baseline mAP=24.26%