NOTE

Please know that this branch has not been updated yet. Interested people may use the cleaning branch of the repository.

Domain Adaptive Faster R-CNN

A PyTorch implementation of the CVPR 2018 paper Domain Adaptive Faster R-CNN for Object Detection in the Wild. The original code used by the authors can be found here.

Usage

Ensure all rerequisites mentioned here are satisfied by your machine. Ensure all images used for training (source) and testing have annotations (in Pascal VOC format). Modify this line in trainval_net_x.py to the directory of the target dataset.

Changes will be made in the future for ease of use.

Train

Add the source and target datasets (in Pascal VOC format) in the src/ and tar/ folders respectively. Modify factory.py so that your dataset is usable. Run training as:

CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_x.py \
                   --src $SOURCE_DATASET_NAME --tar $TARGET_DATASET_NAME \
                   --da True --adaption_lr True --net res101 \
                   --bs $BATCH_SIZE --nw $WORKER_NUMBER \
                   --lr $LEARNING_RATE --lr_decay_step $DECAY_STEP \
                   --cuda

Test

If you want to evaluate the detection performance of a pre-trained model on your testset run:

python test_net_x.py --dataset $TARGET_DATASET_NAME --net res101 \
                   --checksession $SESSION --checkepoch $EPOCH --checkpoint $CHECKPOINT \
                   --cuda

Specify the specific model session, checkepoch and checkpoint, e.g., SESSION=1, EPOCH=5, CHECKPOINT=5931.

Demo

Follow instructions given here

Acknowledgements

This code is built on the python implementation of the Faster-RCNN jwyang/faster-rcnn.pytorch

Part of an ongoing project under Dr. Saket Anand