Tianheng Cheng2,3,*, Lin Song1,📧,*, Yixiao Ge1,🌟,2, Wenyu Liu3, Xinggang Wang3,📧, Ying Shan1,2
\* 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) Open In Colab [![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 **YOLO-World is still under active development!** 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 `yixiaoge@tencent.com`. ## 🔥 Updates `[2025-2-8]:` We release a new YOLO-World-V2.1, which includes new pre-trained weights and training code for image prompts. Please see the update [YOLO-World-V2.1-Blog](./docs/update_20250123.md) for details.\ `[2024-11-5]`: We update the `YOLO-World-Image` and you can try it at HuggingFace [YOLO-World-Image (Preview Version)](https://huggingface.co/spaces/wondervictor/YOLO-World-Image). It's a *preview* version and we are still improving it! Detailed documents about training and few-shot inference are coming soon.\ `[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)!
### Zero-shot Evaluation Results for Pre-trained Models We evaluate all YOLO-World-V2.1 models on LVIS, LVIS-mini, and COCO in the zero-shot manner, and compare with the previous version (the improvements are annotated in the superscripts).
ModelResolutionLVIS APLVIS-miniCOCO
APAPrAPcAPfAPAPrAPcAPfAPAP50AP75
YOLO-World-S64018.5+1.212.615.824.123.6+0.916.421.526.636.651.039.7
YOLO-World-S128019.7+0.913.516.326.325.5+1.419.122.629.338.254.241.6
YOLO-World-M64024.1+0.616.921.130.630.6+0.619.729.034.143.058.646.7
YOLO-World-M128026.0+0.719.922.532.732.7+1.124.430.236.443.860.347.7
YOLO-World-L64026.8+0.719.823.633.433.8+0.924.532.336.844.960.448.9
YOLO-World-L80028.322.524.435.135.227.832.638.847.463.351.8
YOLO-World-L128028.7+1.122.924.935.435.5+1.224.434.038.846.062.550.0
YOLO-World-X64028.6+0.222.025.634.935.8+0.431.033.738.546.762.551.0
YOLO-World-X-1280 is coming soon.
### Model Card
ModelResolutionTrainingDataModel Weights
YOLO-World-S640PT (100e)O365v1+GoldG+CC-LiteV2 🤗 HuggingFace
YOLO-World-S1280CPT (40e)O365v1+GoldG+CC-LiteV2 🤗 HuggingFace
YOLO-World-M640PT (100e)O365v1+GoldG+CC-LiteV2 🤗 HuggingFace
YOLO-World-M1280CPT (40e)O365v1+GoldG+CC-LiteV2 🤗 HuggingFace
YOLO-World-L640PT (100e)O365v1+GoldG+CC-LiteV2 🤗 HuggingFace
YOLO-World-L800 / 1280CPT (40e)O365v1+GoldG+CC-LiteV2 🤗 HuggingFace
YOLO-World-X640PT (100e)O365v1+GoldG+CC-LiteV2 🤗 HuggingFace
**Notes:** * PT: Pre-training, CPT: continuing pre-training * CC-LiteV2: the newly-annotated CC3M subset, including 250k images. ## 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.