This repository contains the codes and models for DiffusionMTL, our multi-task scene understanding model trained with partially annotated data.
Please check the CVPR 2024 paper for more details.
Overview of the proposed DiffusionMTL for multi-task scene understanding.
:triangular_flag_on_post: Updates
We inherit the environement of TaskPrompter, and here is a successful path to deploy it:
conda create -n mtl python=3.7
conda activate mtl
pip install tqdm Pillow easydict pyyaml imageio scikit-image tensorboard termcolor matplotlib
pip install opencv-python==4.5.4.60 setuptools==59.5.0
# Example of installing pytorch-1.10.0
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install timm==0.5.4 einops==0.4.1
We use the same data (PASCAL-Context and NYUD-v2) as ATRC. You can download the data from: PASCALContext.tar.gz, NYUDv2.tar.gz
And then extract the datasets by:
tar xfvz NYUDv2.tar.gz
tar xfvz PASCALContext.tar.gz
You need to put the datasets into one directory and specify the directory as db_root
variable in configs/mypath.py
.
The config files are defined in ./configs
.
Edge evaluation code: https://github.com/prismformore/Boundary-Detection-Evaluation-Tools
Before start training, you need to change the .sh
files for different configuation. We use DDP for multi-gpu training by default. You may need to read realted documents before setting the gpu numbers.
PASCAL-Context:
bash run.sh
To faciliate the community to reproduce our SoTA results, we re-train our best performing models with the training code in this repository and provide the weights for the reserachers.
Version | Dataset | Download | Segmentation (mIoU) | Human parsing (mIoU) | Saliency (maxF) | Normals (mErr) | Boundary (odsF) |
---|---|---|---|---|---|---|---|
DiffusionMTL (Feature Diffusion) | PASCAL-Context (one-label) | onedrive | 57.16 | 59.28 | 78.00 | 16.17 | 64.60 |
Please consider :star2: star our project to share with your community if you find this repository helpful!
BibTex:
@InProceedings{diffusionmtl,
title={DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data},
author={Ye, Hanrong and Xu, Dan},
booktitle={CVPR},
year={2024}
}
Please contact Hanrong Ye if any questions.