Pseudo-Label Diversity Exploitation for Few-Shot Object Detection

The official implementation of Pseudo-Label Diversity Exploitation for Few-Shot Object Detection

Requirements

• Linux with Python >= 3.6
• PyTorch >= 1.3
• torchvision that matches the PyTorch installation
• Dependencies: pip install -r requirements.txt
• pycocotools: pip install cython; pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
• fvcore: pip install 'git+https://github.com/facebookresearch/fvcore'
• OpenCV, optional, needed by demo and visualization pip install opencv-python
• GCC >= 4.9

Train & Inference

Training

1. Stage 1: Training base detector.

python tools/train_net.py --num-gpus 1 \
        --configs/COCO-detection/faster_rcnn_R_101_FPN_base.yaml

2. Random initialize weights for novel classes.

python tools/ckpt_surgery.py \
        --src1 checkpoints/coco/faster_rcnn/faster_rcnn_R_101_FPN/model_final.pth \
        --method randinit \
        --save-dir checkpoints/coco/faster_rcnn/faster_rcnn_R_101_FPN

3. Stage 2: Fine-tune for novel data.

python tools/train_net.py --num-gpus 1 \
        --configs/COCO-detection/faster_rcnn_R_101_FPN_ft_all_10shot.yaml
        --opts MODEL.WEIGHTS WEIGHTS_PATH

4. Stage 3: Fine-tune for pseudo data.

python3 -m tools.genarate_pseudo --num-gpus 1

python3 -m tools.train_feature --num-gpus 1   

Evaluation

To evaluate the trained models, run

python tools/test_net.py --num-gpus 1 \
        --config-file configs/COCO-detection/faster_rcnn_R_101_FPN_ft_all_10shot.yaml \
        --eval-only