Self-Guided Adaptation

Acknowledgment

The implementation is built on the pytorch implementation of faster-rcnn.pytorch, please refer to the original project to set up the environment.

Prerequisites

• Python 2.7 or 3.6
• Pytorch 0.4.0
• CUDA 8.0 or higher

Data Preparation

• PASCAL_VOC 07+12: Please refer py-faster-rcnn for constructing PASCAL VOC Datasets.
• WaterColor: Please refer Cross Domain Detection .
• Night-time(Detrac-night): TBA.
• Citysscape, FoggyCityscape: Please refer Cityscape.
• KITTI: Please refer KITTI

All codes are written to fit for the Data format of Pascal VOC.

Pretrained Model

We used ResNet101 pretrained on the ImageNet in our experiments. You can download the model from:

• ResNet101: Dropbox, VT Server

Well-trained Domain Adaptation Object Detection Models

• Cityscape to KITTI(Res101-based): GoogleDrive
• KITTI to Cityscape(Res101-based): GoogleDrive
• Cityscape to Foggycityscape(Res101-based): GoogleDrive
• Pascal VOC to WaterColor(Res101-based): GoogleDrive
• Daytime(Cityscape) to Night-time(Detrac-Night) (Res101-based): GoogleDrive

Train

• Train SGA with Self-guided adversarial loss and hardness loss:

 CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_auto.py \
                    --dataset source_dataset --dataset_t target_dataset --net res101 \
                    --cuda

• Train SGA with all components(self-guided adversarial loss, hardness loss, self-guided progressive sampling)

 CUDA_VISIBLE_DEVICES=$GPU_ID python trainval_net_auto_self_pace.py \
                    --dataset source_dataset --dataset_t target_dataset --net res101 \
                    --cuda