Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/AILab-CVC/YOLO-World

[CVPR 2024] Real-Time Open-Vocabulary Object Detection
https://github.com/AILab-CVC/YOLO-World
Last synced: 12 days ago
JSON representation
[CVPR 2024] Real-Time Open-Vocabulary Object Detection
Host: GitHub
URL: https://github.com/AILab-CVC/YOLO-World
Owner: AILab-CVC
License: gpl-3.0
Created: 2024-01-29T02:04:07.000Z (9 months ago)
Default Branch: master
Last Pushed: 2024-07-30T14:59:19.000Z (3 months ago)
Last Synced: 2024-10-22T21:54:05.280Z (17 days ago)
Language: Python
Homepage: https://www.yoloworld.cc
Size: 3.86 MB
Stars: 4,548
Watchers: 41
Forks: 444
Open Issues: 300
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

AiTreasureBox - AILab-CVC/YOLO-World - 11-02_4607_0](https://img.shields.io/github/stars/AILab-CVC/YOLO-World.svg)|Real-Time Open-Vocabulary Object Detection| (Repos)
awesome-yolo-object-detection - YOLO-World - CVC/YOLO-World?style=social"/> : "YOLO-World: Real-Time Open-Vocabulary Object Detection". (**[CVPR 2024](https://arxiv.org/abs/2401.17270)**). [www.yoloworld.cc](https://www.yoloworld.cc/) (Summary)
awesome-yolo-object-detection - YOLO-World - CVC/YOLO-World?style=social"/> : "YOLO-World: Real-Time Open-Vocabulary Object Detection". (**[CVPR 2024](https://arxiv.org/abs/2401.17270)**). [www.yoloworld.cc](https://www.yoloworld.cc/) (Summary)
README

        







Tianheng Cheng^2,3,*, 

Lin Song^1,📧,*,

Yixiao Ge^1,🌟,2,

 Wenyu Liu³,

Xinggang Wang^3,📧,

Ying Shan^1,2

\* Equal contribution 🌟 Project lead 📧 Corresponding author

¹ Tencent AI Lab,  ² ARC Lab, Tencent PCG

³ Huazhong University of Science and Technology






[![arxiv paper](https://img.shields.io/badge/Project-Page-green)](https://wondervictor.github.io/)

[![arxiv paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2401.17270)



[![demo](https://img.shields.io/badge/🤗HugginngFace-Spaces-orange)](https://huggingface.co/spaces/stevengrove/YOLO-World)

[![Replicate](https://replicate.com/zsxkib/yolo-world/badge)](https://replicate.com/zsxkib/yolo-world)

[![hfpaper](https://img.shields.io/badge/🤗HugginngFace-Paper-yellow)](https://huggingface.co/papers/2401.17270)

[![license](https://img.shields.io/badge/License-GPLv3.0-blue)](LICENSE)

[![yoloworldseg](https://img.shields.io/badge/YOLOWorldxEfficientSAM-🤗Spaces-orange)](https://huggingface.co/spaces/SkalskiP/YOLO-World)

[![yologuide](https://img.shields.io/badge/📖Notebook-roboflow-purple)](https://supervision.roboflow.com/develop/notebooks/zero-shot-object-detection-with-yolo-world)

[![deploy](https://media.roboflow.com/deploy.svg)](https://inference.roboflow.com/foundation/yolo_world/)





## Notice

We recommend that everyone **use English to communicate on issues**, as this helps developers from around the world discuss, share experiences, and answer questions together.

For business licensing and other related inquiries, don't hesitate to contact `[email protected]`.

## 🔥 Updates 

`[2024-7-8]`: YOLO-World now has been integrated into [ComfyUI](https://github.com/StevenGrove/ComfyUI-YOLOWorld)! Come and try adding YOLO-World to your workflow now! You can access it at [StevenGrove/ComfyUI-YOLOWorld](https://github.com/StevenGrove/ComfyUI-YOLOWorld)!  

`[2024-5-18]:` YOLO-World models have been [integrated with the FiftyOne computer vision toolkit](https://docs.voxel51.com/integrations/ultralytics.html#open-vocabulary-detection) for streamlined open-vocabulary inference across image and video datasets.  

`[2024-5-16]:` Hey guys! Long time no see! This update contains (1) [fine-tuning guide](https://github.com/AILab-CVC/YOLO-World?#highlights--introduction) and (2) [TFLite Export](./docs/tflite_deploy.md) with INT8 Quantization.  

`[2024-5-9]:` This update contains the real [`reparameterization`](./docs/reparameterize.md) 🪄, and it's better for fine-tuning on custom datasets and improves the training/inference efficiency 🚀!  

`[2024-4-28]:` Long time no see! This update contains bugfixs and improvements: (1) ONNX demo; (2) image demo (support tensor input); (2) new pre-trained models; (3) image prompts; (4) simple version for fine-tuning / deployment; (5) guide for installation (include a `requirements.txt`).  

`[2024-3-28]:` We provide: (1) more high-resolution pre-trained models (e.g., S, M, X) ([#142](https://github.com/AILab-CVC/YOLO-World/issues/142)); (2) pre-trained models with CLIP-Large text encoders. Most importantly, we preliminarily fix the **fine-tuning without `mask-refine`** and explore a new fine-tuning setting ([#160](https://github.com/AILab-CVC/YOLO-World/issues/160),[#76](https://github.com/AILab-CVC/YOLO-World/issues/76)). In addition, fine-tuning YOLO-World with `mask-refine` also obtains significant improvements, check more details in [configs/finetune_coco](./configs/finetune_coco/).  

`[2024-3-16]:` We fix the bugs about the demo ([#110](https://github.com/AILab-CVC/YOLO-World/issues/110),[#94](https://github.com/AILab-CVC/YOLO-World/issues/94),[#129](https://github.com/AILab-CVC/YOLO-World/issues/129), [#125](https://github.com/AILab-CVC/YOLO-World/issues/125)) with visualizations of segmentation masks, and release [**YOLO-World with Embeddings**](./docs/prompt_yolo_world.md), which supports prompt tuning, text prompts and image prompts.  

`[2024-3-3]:` We add the **high-resolution YOLO-World**, which supports `1280x1280` resolution with higher accuracy and better performance for small objects!  

`[2024-2-29]:` We release the newest version of [ **YOLO-World-v2**](./docs/updates.md) with higher accuracy and faster speed! We hope the community can join us to improve YOLO-World!  

`[2024-2-28]:` Excited to announce that YOLO-World has been accepted by **CVPR 2024**! We're continuing to make YOLO-World faster and stronger, as well as making it better to use for all.  

`[2024-2-22]:` We sincerely thank [RoboFlow](https://roboflow.com/) and [@Skalskip92](https://twitter.com/skalskip92) for the [**Video Guide**](https://www.youtube.com/watch?v=X7gKBGVz4vs) about YOLO-World, nice work!  

`[2024-2-18]:` We thank [@Skalskip92](https://twitter.com/skalskip92) for developing the wonderful segmentation demo via connecting YOLO-World and EfficientSAM. You can try it now at the [🤗 HuggingFace Spaces](https://huggingface.co/spaces/SkalskiP/YOLO-World).   

`[2024-2-17]:` The largest model **X** of YOLO-World is released, which achieves better zero-shot performance!   

`[2024-2-17]:` We release the code & models for **YOLO-World-Seg** now! YOLO-World now supports open-vocabulary / zero-shot object segmentation!  

`[2024-2-15]:` The pre-traind YOLO-World-L with CC3M-Lite is released!     

`[2024-2-14]:` We provide the [`image_demo`](demo.py) for inference on images or directories.   

`[2024-2-10]:` We provide the [fine-tuning](./docs/finetuning.md) and [data](./docs/data.md) details for fine-tuning YOLO-World on the COCO dataset or the custom datasets!  

`[2024-2-3]:` We support the `Gradio` demo now in the repo and you can build the YOLO-World demo on your own device!  

`[2024-2-1]:` We've released the code and weights of YOLO-World now!  

`[2024-2-1]:` We deploy the YOLO-World demo on [HuggingFace 🤗](https://huggingface.co/spaces/stevengrove/YOLO-World), you can try it now!  

`[2024-1-31]:` We are excited to launch **YOLO-World**, a cutting-edge real-time open-vocabulary object detector.  

## TODO

YOLO-World is under active development and please stay tuned ☕️! 

If you have suggestions📃 or ideas💡,**we would love for you to bring them up in the [Roadmap](https://github.com/AILab-CVC/YOLO-World/issues/109)** ❤️!

> YOLO-World 目前正在积极开发中📃，如果你有建议或者想法💡，**我们非常希望您在 [Roadmap](https://github.com/AILab-CVC/YOLO-World/issues/109) 中提出来** ❤️！

## [FAQ (Frequently Asked Questions)](https://github.com/AILab-CVC/YOLO-World/discussions/149)

We have set up an FAQ about YOLO-World in the discussion on GitHub. We hope everyone can raise issues or solutions during use here, and we also hope that everyone can quickly find solutions from it.

> 我们在GitHub的discussion中建立了关于YOLO-World的常见问答，这里将收集一些常见问题，同时大家可以在此提出使用中的问题或者解决方案，也希望大家能够从中快速寻找到解决方案

## Highlights & Introduction

This repo contains the PyTorch implementation, pre-trained weights, and pre-training/fine-tuning code for YOLO-World.

* YOLO-World is pre-trained on large-scale datasets, including detection, grounding, and image-text datasets.

* YOLO-World is the next-generation YOLO detector, with a strong open-vocabulary detection capability and grounding ability.

* YOLO-World presents a *prompt-then-detect* paradigm for efficient user-vocabulary inference, which re-parameterizes vocabulary embeddings as parameters into the model and achieve superior inference speed. You can try to export your own detection model without extra training or fine-tuning in our [online demo](https://huggingface.co/spaces/stevengrove/YOLO-World)!







## Model Zoo

We've pre-trained YOLO-World-S/M/L from scratch and evaluate on the `LVIS val-1.0` and `LVIS minival`. We provide the pre-trained model weights and training logs for applications/research or re-producing the results.

### Zero-shot Inference on LVIS dataset



| model                                                                                                                | Pre-train Data       | Size | AP^{mini | AP_r | AP_c | AP_f | AP^{val | AP_r | AP_c | AP_f |                                                                                        weights                                                                                         |

| :------------------------------------------------------------------------------------------------------------------- | :------------------- | :----------------- | :--------------: | :------------: | :------------: | :------------: | :-------------: | :------------: | :------------: | :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |

| [YOLO-Worldv2-S](./configs/pretrain/yolo_world_v2_s_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py) | O365+GoldG | 640 | 22.7 | 16.3 | 20.8 | 25.5 |  17.3 | 11.3 | 14.9 | 22.7 |[HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_s_obj365v1_goldg_pretrain-55b943ea.pth)|

| [YOLO-Worldv2-S](./configs/pretrain/yolo_world_v2_s_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py) | O365+GoldG | 1280🔸 | 24.1 | 18.7 | 22.0 | 26.9 |  18.8 | 14.1 | 16.3 | 23.8 |[HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_s_obj365v1_goldg_pretrain_1280ft-fc4ff4f7.pth)|  

| [YOLO-Worldv2-M](./configs/pretrain/yolo_world_v2_m_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py)  | O365+GoldG | 640 | 30.0 | 25.0  | 27.2 | 33.4 | 23.5 | 17.1 | 20.0 | 30.1 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_m_obj365v1_goldg_pretrain-c6237d5b.pth)| 

| [YOLO-Worldv2-M](./configs/pretrain/yolo_world_v2_m_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py) | O365+GoldG | 1280🔸 | 31.6 | 24.5  | 29.0 | 35.1 | 25.3 | 19.3 | 22.0 | 31.7 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_m_obj365v1_goldg_pretrain_1280ft-77d0346d.pth)| 

| [YOLO-Worldv2-L](./configs/pretrain/yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py) | O365+GoldG | 640 | 33.0 | 22.6 | 32.0 | 35.8 | 26.0 | 18.6 | 23.0 | 32.6 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_obj365v1_goldg_pretrain-a82b1fe3.pth)| 

| [YOLO-Worldv2-L](./configs/pretrain/yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py) | O365+GoldG | 1280🔸 | 34.6 | 29.2 | 32.8 | 37.2 | 27.6 | 21.9 | 24.2 | 34.0 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_obj365v1_goldg_pretrain_1280ft-9babe3f6.pth)| 

| [YOLO-Worldv2-L (CLIP-Large)](./configs/pretrain/yolo_world_v2_l_clip_large_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py) 🔥  | O365+GoldG | 640 | 34.0 | 22.0 | 32.6 | 37.4 | 27.1 | 19.9 | 23.9 | 33.9 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_clip_large_o365v1_goldg_pretrain-8ff2e744.pth)|

| [YOLO-Worldv2-L (CLIP-Large)](./configs/pretrain/yolo_world_v2_l_clip_large_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_800ft_lvis_minival.py) 🔥  | O365+GoldG | 800🔸 | 35.5 | 28.3 | 33.2 | 38.8 | 28.6 | 22.0 | 25.1 | 35.4 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_clip_large_o365v1_goldg_pretrain_800ft-9df82e55.pth)|

| [YOLO-Worldv2-L](./configs/pretrain/yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py) | O365+GoldG+CC3M-Lite | 640 | 32.9 | 25.3 | 31.1 | 35.8 | 26.1 | 20.6 | 22.6 | 32.3 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_l_obj365v1_goldg_cc3mlite_pretrain-ca93cd1f.pth)|

| [YOLO-Worldv2-X](./configs/pretrain/yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py) | O365+GoldG+CC3M-Lite | 640 | 35.4 | 28.7 | 32.9 | 38.7 | 28.4 | 20.6 | 25.6 | 35.0 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_x_obj365v1_goldg_cc3mlite_pretrain-8698fbfa.pth) |

| 🔥 [YOLO-Worldv2-X]() |  O365+GoldG+CC3M-Lite | 1280🔸 | 37.4 | 30.5 | 35.2 | 40.7  | 29.8 | 21.1 | 26.8 | 37.0 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_x_obj365v1_goldg_cc3mlite_pretrain_1280ft-14996a36.pth) |

| [YOLO-Worldv2-XL](./configs/pretrain/yolo_world_v2_xl_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py) | O365+GoldG+CC3M-Lite | 640 | 36.0 | 25.8 | 34.1 | 39.5 | 29.1 | 21.1 | 26.3 | 35.8 | [HF Checkpoints 🤗](https://huggingface.co/wondervictor/YOLO-World/blob/main/yolo_world_v2_xl_obj365v1_goldg_cc3mlite_pretrain-5daf1395.pth) |}}





**NOTE:**

1. AP^mini: evaluated on LVIS `minival`.

3. AP^val: evaluated on LVIS `val 1.0`.

4. [HuggingFace Mirror](https://hf-mirror.com/) provides the mirror of HuggingFace, which is a choice for users who are unable to reach.

5. 🔸: fine-tuning models with the pre-trained data.

**Pre-training Logs:**

We provide the pre-training logs of `YOLO-World-v2`. Due to the unexpected errors of the local machines, the training might be interrupted several times.

| Model | YOLO-World-v2-S | YOLO-World-v2-M  | YOLO-World-v2-L | YOLO-World-v2-X |

| :---  | :-------------: | :--------------: | :-------------: | :-------------: |

|Pre-training Log | [Part-1](https://drive.google.com/file/d/1oib7pKfA2h1U_5-85H_s0Nz8jWd0R-WP/view?usp=drive_link), [Part-2](https://drive.google.com/file/d/11cZ6OZy80VTvBlZy3kzLAHCxx5Iix5-n/view?usp=drive_link) | [Part-1](https://drive.google.com/file/d/1E6vYSS8kBipGc8oQnsjAfeUAx8I9yOX7/view?usp=drive_link), [Part-2](https://drive.google.com/file/d/1fbM7vt2tgSeB8o_7tUDofWvpPNSViNj5/view?usp=drive_link) | [Part-1](https://drive.google.com/file/d/1Tola1QGJZTL6nGy3SBxKuknfNfREDm8J/view?usp=drive_link), [Part-2](https://drive.google.com/file/d/1mTBXniioUb0CdctCG4ckIU6idGo0NnH8/view?usp=drive_link) |  [Final part](https://drive.google.com/file/d/1aEUA_EPQbXOrpxHTQYB6ieGXudb1PLpd/view?usp=drive_link)| 

## Getting started

### 1. Installation

YOLO-World is developed based on `torch==1.11.0` `mmyolo==0.6.0` and `mmdetection==3.0.0`. Check more details about `requirements` and `mmcv` in [docs/installation](./docs/installation.md).

#### Clone Project 

```bash

git clone --recursive https://github.com/AILab-CVC/YOLO-World.git

```

#### Install

```bash

pip install torch wheel -q

pip install -e .

```

### 2. Preparing Data

We provide the details about the pre-training data in [docs/data](./docs/data.md).

## Training & Evaluation

We adopt the default [training](./tools/train.py) or [evaluation](./tools/test.py) scripts of [mmyolo](https://github.com/open-mmlab/mmyolo).

We provide the configs for pre-training and fine-tuning in `configs/pretrain` and `configs/finetune_coco`.

Training YOLO-World is easy:

```bash

chmod +x tools/dist_train.sh

# sample command for pre-training, use AMP for mixed-precision training

./tools/dist_train.sh configs/pretrain/yolo_world_l_t2i_bn_2e-4_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 8 --amp

```

**NOTE:** YOLO-World is pre-trained on 4 nodes with 8 GPUs per node (32 GPUs in total). For pre-training, the `node_rank` and `nnodes` for multi-node training should be specified. 

Evaluating YOLO-World is also easy:

```bash

chmod +x tools/dist_test.sh

./tools/dist_test.sh path/to/config path/to/weights 8

```

**NOTE:** We mainly evaluate the performance on LVIS-minival for pre-training.

## Fine-tuning YOLO-World









Chose your pre-trained YOLO-World and Fine-tune it! 



YOLO-World supports **zero-shot inference**, and three types of **fine-tuning recipes**: **(1) normal fine-tuning**, **(2) prompt tuning**, and **(3) reparameterized fine-tuning**.

* Normal Fine-tuning: we provide the details about fine-tuning YOLO-World in [docs/fine-tuning](./docs/finetuning.md).

* Prompt Tuning: we provide more details ahout prompt tuning in [docs/prompt_yolo_world](./docs/prompt_yolo_world.md).

* Reparameterized Fine-tuning: the reparameterized YOLO-World is more suitable for specific domains far from generic scenes. You can find more details in [docs/reparameterize](./docs/reparameterize.md).

## Deployment

We provide the details about deployment for downstream applications in [docs/deployment](./docs/deploy.md).

You can directly download the ONNX model through the online [demo](https://huggingface.co/spaces/stevengrove/YOLO-World) in Huggingface Spaces 🤗.

- [x] ONNX export and demo: [docs/deploy](https://github.com/AILab-CVC/YOLO-World/blob/master/docs/deploy.md)

- [x] TFLite and INT8 Quantization: [docs/tflite_deploy](https://github.com/AILab-CVC/YOLO-World/blob/master/docs/tflite_deploy.md)

- [ ] TensorRT: coming soon.

- [ ] C++: coming soon.

## Demo

See [`demo`](./demo) for more details

- [x] `gradio_demo.py`: Gradio demo, ONNX export

- [x] `image_demo.py`: inference with images or a directory of images

- [x] `simple_demo.py`: a simple demo of YOLO-World, using `array` (instead of path as input).

- [x] `video_demo.py`: inference YOLO-World on videos.

- [x] `inference.ipynb`: jupyter notebook for YOLO-World.

- [x] [Google Colab Notebook](https://colab.research.google.com/drive/1F_7S5lSaFM06irBCZqjhbN7MpUXo6WwO?usp=sharing): We sincerely thank [Onuralp](https://github.com/onuralpszr) for sharing the [Colab Demo](https://colab.research.google.com/drive/1F_7S5lSaFM06irBCZqjhbN7MpUXo6WwO?usp=sharing), you can have a try 😊！

## Acknowledgement

We sincerely thank [mmyolo](https://github.com/open-mmlab/mmyolo), [mmdetection](https://github.com/open-mmlab/mmdetection), [GLIP](https://github.com/microsoft/GLIP), and [transformers](https://github.com/huggingface/transformers) for providing their wonderful code to the community!

## Citations

If you find YOLO-World is useful in your research or applications, please consider giving us a star 🌟 and citing it.

```bibtex

@inproceedings{Cheng2024YOLOWorld,

  title={YOLO-World: Real-Time Open-Vocabulary Object Detection},

  author={Cheng, Tianheng and Song, Lin and Ge, Yixiao and Liu, Wenyu and Wang, Xinggang and Shan, Ying},

  booktitle={Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},

  year={2024}

}

```

## Licence

YOLO-World is under the GPL-v3 Licence and is supported for commercial usage. If you need a commercial license for YOLO-World, please feel free to contact us.