Awesome

SegRefiner

This is the official pytorch implementation of SegRefiner built on the open-source MMDetection.

SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
Mengyu Wang, Henghui Ding, Jun Hao Liew, Jiajun Liu, Yao Zhao, Yunchao Wei
NeurIPS, 2023

Highlights

• SegRefiner: A universally applicable refinement methodology designed to augment the segmentation accuracy across diverse segmentation tasks and models.
• Novelty: Regarding segmentation refinement as a denoising procedure and executing it via a discrete diffusion model.
• Efficacy: Consistently enhancing the accuracy across various segmentation tasks and models, with the capability to capture exceedingly fine details.

Installation

conda create -n segrefiner python=3.8
conda activate segrefiner

# torch
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

# mmcv and mmdet
pip install openmim
mim install "mmcv-full==1.7.1"
pip install -e .

# for boundary AP evaluation
git clone -b lvis_challenge_2021 https://github.com/lvis-dataset/lvis-api.git
cd lvis-api
pip install -e .

Dataset Preparation

Please download the COCO dataset, LVIS annotations, ThinObject-5K dataset, BIG dataset and DIS-5K dataset first and place them in the "data" directory. The structure of the "data" folder should be as follows:

data
├── big
│   ├── test
│   └── val
├── dis
│   ├── DIS-TE1
│   ├── DIS-TE2
│   ├── DIS-TE3
│   ├── DIS-TE4
│   ├── DIS-TR
│   └── DIS-VD
├── thin_object
│   ├── images
│   ├── list
│   └── masks
├── coco
│   ├── train2017
│   ├── val2017
│   └── annotations
|       ├── instances_train2017.json
│       └── instances_val2017.json
└── lvis
    └── annotations
        ├── lvis_v1_train.json
        └── lvis_v1_val.json

Training

LR-SegRefiner

The LR-SegRefiner is trained on the LVIS dataset, with the coarse masks employed in training generated in an online manner. Consequently, the training process can be initiated as follows:

# on one GPU
python tools/train.py configs/segrefiner/segrefiner_lr.py
# or on multiple GPUs
bash tools/dist_train.sh configs/segrefiner/segrefiner_lr.py 8

HR-SegRefiner

The HR-SegRefiner is trained on a high-resolution dataset comprising ThinObject-5K and DIS-5K. Given the potential time consumption associated with online coarse masks generation in such high-resolution datasets, the coarse masks used in training are produced offline by executing:

python scripts/gen_coarse_masks_hr.py

The produced coarse masks will be saved in the data/dis/DIS-TR and data/thin_object direction. Subsequently, training can be initiated by executing:

# on one GPU
python tools/train.py configs/segrefiner/segrefiner_hr.py
# or on multiple GPUs
bash tools/dist_train.sh configs/segrefiner/segrefiner_hr.py 8

Evaluation

The trained models are provided in LR-SegRefiner and HR-SegRefiner.

For the inference phase, it is necessary to initially prepare the results produced by previous segmentation models as input for our SegRefiner. We provide the the instance segmentation results we used in our experiments, most of which are produced by the MMDetection implementations. The input of BIG dataset and DIS-5K dataset can be accessed through BIG and DIS-5K.

COCO

To assess SegRefiner on the COCO dataset, initiate the evaluation process by executing the provided test script. This will generate refined results and save them in a JSON file.

# on one GPU
python tools/test.py configs/segrefiner/segrefiner_coco.py checkpoints/segrefiner_lr_latest.pth --out_prefix test_model_name
# or on multiple GPUs
bash tools/dist_test.sh configs/segrefiner/segrefiner_coco.py checkpoints/segrefiner_lr_latest.pth 8 --out_prefix test_model_name

Subsequently, execute the following evaluation script to obtain the metrics.

python scripts/eval_json.py

BIG and DIS-5K

To assess SegRefiner on the BIG or DIS-5k datasets, begin by executing the following test script to obtain refined results, saving them in a folder in .png format.

# on one GPU
python tools/test.py \
configs/segrefiner/segrefiner_big.py \ # for BIG dataset
configs/segrefiner/segrefiner_dis.py \ # for DIS-5K dataset
checkpoints/segrefiner_hr_latest.pth --out_dir test_model_name
# or on multiple GPUs
bash tools/dist_test.sh \
configs/segrefiner/segrefiner_big.py \ # for BIG dataset
configs/segrefiner/segrefiner_dis.py \ # for DIS-5K dataset
checkpoints/segrefiner_hr_latest.pth 8 --out_dir test_model_name

Following that, run the provided evaluation scripts to acquire the corresponding metrics.

# for BIG dataset
python scripts/eval_miou.py
# for DIS-5K dataset
python scripts/eval_miou_dis.py

Results on BIG dataset

Results on COCO val-set (with LVIS annotation)

Results on DIS5K dataset

BibTeX

Please consider to cite SegRefiner if it helps your research.

@inproceedings{SegRefiner,
  title={{SegRefiner}: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process},
  author={Wang, Mengyu and Ding, Henghui and Liew, Jun Hao and Liu, Jiajun and Zhao, Yao and Wei, Yunchao},
  booktitle={NeurIPS},
  year={2023}
}