DuPL

This repository contains the source code of CVPR 2024 paper: "DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation".

📢 Update Log

• Mar. 21, 2024 (U2): Add the evaluation / visualization scripts for CAM and segmentation inference.
• Mar. 21, 2024 (U1): The pre-trained checkpoints and segmentation results released 🔥🔥🔥.
• Mar. 17, 2024: Basic training code released.

Get Started

Training Environment

The implementation is based on PyTorch 1.13.1 with single-node multi-gpu training. Please install the required packages by running:

pip install -r requirements.txt

Datasets

Experiments

Training DuPL

To train the segmentation model for the VOC dataset, please run

# For Pascal VOC 2012
python -m torch.distributed.run --nproc_per_node=2 train_final_voc.py --data_folder [../VOC2012]

NOTE:

• The vaild batch size should be 4 (num_gpus * sampler_per_gpu).
• The --nproc_per_node should be set according to your environment (recommend: 2x NVIDIA RTX 3090 GPUs).

To train the segmentation model for the MS COCO dataset, please run

# For MSCOCO
python -m torch.distributed.run --nproc_per_node=4 train_final_coco.py --data_folder [../MSCOCO/coco2014]

NOTE:

• The vaild batch size should be 8 (num_gpus * sampler_per_gpu).
• The --nproc_per_node should be set according to your environment (recommend: 4x NVIDIA RTX 3090 GPUs).

Evaluation

Please install pydensecrf first:

pip install git+https://github.com/lucasb-eyer/pydensecrf.git

NOTE: using pip install pydensecrf will install an incompatible version ⚠️.

To evaluate the trained model, please run:

# For Pascal VOC
python eval_seg_voc.py --data_folder [../VOC2012] --model_path [path_to_model]

# For MSCOCO
python -m torch.distributed.launch --nproc_per_node=4 eval_seg_coco_ddp.py --data_folder [../MSCOCO/coco2014] --label_folder [../MSCOCO/SegmentationClass] --model_path [path_to_model]

NOTE:

• The segmentation results will be saved at the checkpoint directory
• DuPL has two independent models (students), and we select the best one for evaluation. IN FACT, we can use some tricks, such as ensemble or model soup, to further improve the performance (maybe).

Convert rgb segmentation labels for the official VOC evaluation:

# modify the "dir" and "target_dir" before running
python convert_voc_rgb.py

CAM inference & evaluation:

python infer_cam_voc.py --data_folder [../VOC2012] --model_path [path_to_model]

NOTE: The CAM results will be saved at the checkpoint directory.

TIPS:

• The evaluation on MS COCO use DDP to accelerate the evaluation stage. Please make sure the torch.distributed.launch is available in your environment.
• We highly recommend use high-performance CPU for CRF post-processing. This processing is quite time-consuming. On MS COCO, it may cost several hours for CRF post-processing.

Results

Checkpoints

We have provided DuPL's pre-trained checkpoints on VOC and COCO datasets. With these checkpoints, it should be expected to reproduce the exact performance listed below.

The VOC test results are evaluated on the official server, and the result links are provided in the paper.

Visualization

We have provided the visualization of CAMs and segmentation images (RGB) on VOC 2012 (val and test) and MS COCO in the following links. Hope they can help you to easily compare with other works :)

Citation

Please kindly cite our paper if you find it's helpful in your work:

@inproceedings{wu2024dupl,
  title={DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation},
  author={Wu, Yuanchen and Ye, Xichen and Yang, Kequan and Li, Jide and Li, Xiaoqiang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3534--3543},
  year={2024}
}

Acknowledgement

We would like to thank all the researchers who open source their works to make this project possible, especially thanks to the authors of Toco for their brilliant work.