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[CVPR 2025 Highlight] Official code for "Olympus: A Universal Task Router for Computer Vision Tasks"
| Date | Stars |
|---|---|
| 2026-07-24 | 428 |
| 2026-07-25 | 428 |
| 2026-07-28 | 428 |
| 2026-07-30 | 428 |
| 2026-08-06 | 428 |
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<p align="center"><img src="https://github.com/yuanze-lin/Olympus/blob/main/asset/olympus.png" alt="icon" width="150" height="150" style="vertical-align:middle; margin-right:5px;" /></p> # Olympus: A Universal Task Router for Computer Vision Tasks (CVPR 2025, Highlight) <br/> [](https://arxiv.org/pdf/2412.09612) [](https://arxiv.org/abs/2412.09612) [](https://yuanze-lin.me/Olympus_page/) [](https://huggingface.co/Yuanze/Olympus) [](https://huggingface.co/datasets/Yuanze/Olympus) [](https://www.youtube.com/watch?v=N1xOdIrVvn4) Official implementation of "Olympus: A Universal Task Router for Computer Vision Tasks" [Yuanze Lin](https://yuanze-lin.me/), [Yunsheng Li](https://scholar.google.com/citations?user=hJrIyCwAAAAJ&hl=en), [Dongdong Chen](https://www.dongdongchen.bid/), [Weijian Xu](https://weijianxu.com/), [Ronald Clark](https://www.ron-clark.com/), [Philip H. S. Torr](https://eng.ox.ac.uk/people/philip-torr/) **:hearts: If you find our project is helpful for your research, please kindly give us a :star2: and cite our paper :bookmark_tabs: : )** ## :mega: News - [ ] Release the code for integration with task-specific models. - [x] Release the training & inference code. - [x] Release Olympus datasets. - [x] Release the model of Olympus. ## :low_brightness: Overview  ## Getting Started ### :hammer_and_wrench: Environment Installation <a href="#install" id="install"/> To establish the environment, just run this code in the shell: ``` git clone https://github.com/yuanze-lin/Olympus.git cd Olympus conda create -n olympus python==3.10 -y conda activate olympus pip install -r requirements.txt ``` That will create the environment ```olympus``` we used. ### Download Models & Data ### We share our collected Olympus dataset as follows: | Instruction | Link | |---------|------| | Olympus Dataset | [Olympus_dataset](https://huggingface.co/datasets/Yuanze/Olympus) | | Olympus Fine-tuning Data | [Olympus.json](https://huggingface.co/datasets/Yuanze/Olympus/blob/main/Olympus.json) | - ```Olympus_dataset```: There are 20 JSON files under ```20 individual tasks``` folder, each corresponding to a specific task. You can refer to the routing token definitions in our paper to identify the task associated with each JSON file, along with the chain-of-action data provided in ```coa.json```. Each of these 21 JSON files includes both training and test data. ```OlympusInstruct.json``` and ```OlympusBench.json``` contain the collected OlympusInstruct and OlympusBench datasets, respectively. - ```Olympus.json```: The final instruction data for fine-tuning. (1) Download the Olympus model: ``` python download_olympus.py ``` It will save the ```Olympus``` model under the ```ckpts``` folder. (2) Download the Olympus data for fine-tuning: ``` python download_olympus_dataset.py ``` It saves the fine-tuning instruction data ```Olympus.json``` to the ```train_data``` folder, while all other JSON files are stored in the newly created ```jsons``` folder. Note that ```Olympus.json``` is a combination of ```llava_v1_5_mix665k.json``` and OlympusInstruct, our collected instruction data covering 20 tasks. **If you want to merge the data manually, download [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json) int
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