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
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For business licensing and other related inquiries, don't hesitate to contact yixiaoge@tencent.com.
[2024-7-8]: YOLO-World now has been integrated into ComfyUI! Come and try adding YOLO-World to your workflow now! You can access it at StevenGrove/ComfyUI-YOLOWorld!
[2024-5-18]: YOLO-World models have been integrated with the FiftyOne computer vision toolkit 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 and (2) TFLite Export with INT8 Quantization.
[2024-5-9]: This update contains the real reparameterization 🪄, 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); (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,#76). In addition, fine-tuning YOLO-World with mask-refine also obtains significant improvements, check more details in configs/finetune_coco.
[2024-3-16]: We fix the bugs about the demo (#110,#94,#129, #125) with visualizations of segmentation masks, and release YOLO-World with Embeddings, 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 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 and @Skalskip92 for the Video Guide about YOLO-World, nice work!
[2024-2-18]: We thank @Skalskip92 for developing the wonderful segmentation demo via connecting YOLO-World and EfficientSAM. You can try it now at the 🤗 HuggingFace Spaces.
[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 for inference on images or directories.
[2024-2-10]: We provide the fine-tuning and data 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 🤗, you can try it now!
[2024-1-31]: We are excited to launch YOLO-World, a cutting-edge real-time open-vocabulary object detector.
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 ❤️!
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 中提出来 ❤️！
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
This repo contains the PyTorch implementation, pre-trained weights, and pre-training/fine-tuning code for YOLO-World.
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YOLO-World is pre-trained on large-scale datasets, including detection, grounding, and image-text datasets.
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YOLO-World is the next-generation YOLO detector, with a strong open-vocabulary detection capability and grounding ability.
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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!
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.
| model | Pre-train Data | Size | APmini | APr | APc | APf | APval | APr | APc | APf | weights |
|---|---|---|---|---|---|---|---|---|---|---|---|
| YOLO-Worldv2-S | O365+GoldG | 640 | 22.7 | 16.3 | 20.8 | 25.5 | 17.3 | 11.3 | 14.9 | 22.7 | HF Checkpoints 🤗 |
| YOLO-Worldv2-S | O365+GoldG | 1280🔸 | 24.1 | 18.7 | 22.0 | 26.9 | 18.8 | 14.1 | 16.3 | 23.8 | HF Checkpoints 🤗 |
| YOLO-Worldv2-M | O365+GoldG | 640 | 30.0 | 25.0 | 27.2 | 33.4 | 23.5 | 17.1 | 20.0 | 30.1 | HF Checkpoints 🤗 |
| YOLO-Worldv2-M | O365+GoldG | 1280🔸 | 31.6 | 24.5 | 29.0 | 35.1 | 25.3 | 19.3 | 22.0 | 31.7 | HF Checkpoints 🤗 |
| YOLO-Worldv2-L | O365+GoldG | 640 | 33.0 | 22.6 | 32.0 | 35.8 | 26.0 | 18.6 | 23.0 | 32.6 | HF Checkpoints 🤗 |
| YOLO-Worldv2-L | O365+GoldG | 1280🔸 | 34.6 | 29.2 | 32.8 | 37.2 | 27.6 | 21.9 | 24.2 | 34.0 | HF Checkpoints 🤗 |
| YOLO-Worldv2-L (CLIP-Large) 🔥 | O365+GoldG | 640 | 34.0 | 22.0 | 32.6 | 37.4 | 27.1 | 19.9 | 23.9 | 33.9 | HF Checkpoints 🤗 |
| YOLO-Worldv2-L (CLIP-Large) 🔥 | O365+GoldG | 800🔸 | 35.5 | 28.3 | 33.2 | 38.8 | 28.6 | 22.0 | 25.1 | 35.4 | HF Checkpoints 🤗 |
| YOLO-Worldv2-L | O365+GoldG+CC3M-Lite | 640 | 32.9 | 25.3 | 31.1 | 35.8 | 26.1 | 20.6 | 22.6 | 32.3 | HF Checkpoints 🤗 |
| YOLO-Worldv2-X | O365+GoldG+CC3M-Lite | 640 | 35.4 | 28.7 | 32.9 | 38.7 | 28.4 | 20.6 | 25.6 | 35.0 | HF Checkpoints 🤗 |
| 🔥 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 🤗 |
| YOLO-Worldv2-XL | O365+GoldG+CC3M-Lite | 640 | 36.0 | 25.8 | 34.1 | 39.5 | 29.1 | 21.1 | 26.3 | 35.8 | HF Checkpoints 🤗 |
NOTE:
- APmini: evaluated on LVIS
minival. - APval: evaluated on LVIS
val 1.0. - HuggingFace Mirror provides the mirror of HuggingFace, which is a choice for users who are unable to reach.
- 🔸: 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, Part-2 | Part-1, Part-2 | Part-1, Part-2 | Final part |
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.
git clone --recursive https://github.com/AILab-CVC/YOLO-World.gitpip install torch wheel -q
pip install -e .We provide the details about the pre-training data in docs/data.
We adopt the default training or evaluation scripts of mmyolo.
We provide the configs for pre-training and fine-tuning in configs/pretrain and configs/finetune_coco.
Training YOLO-World is easy:
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 --ampNOTE: 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:
chmod +x tools/dist_test.sh
./tools/dist_test.sh path/to/config path/to/weights 8NOTE: We mainly evaluate the performance on LVIS-minival for pre-training.
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.
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Normal Fine-tuning: we provide the details about fine-tuning YOLO-World in docs/fine-tuning.
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Prompt Tuning: we provide more details ahout prompt tuning in docs/prompt_yolo_world.
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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.
We provide the details about deployment for downstream applications in docs/deployment. You can directly download the ONNX model through the online demo in Huggingface Spaces 🤗.
- ONNX export and demo: docs/deploy
- TFLite and INT8 Quantization: docs/tflite_deploy
- TensorRT: coming soon.
- C++: coming soon.
See demo for more details
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gradio_demo.py: Gradio demo, ONNX export -
image_demo.py: inference with images or a directory of images -
simple_demo.py: a simple demo of YOLO-World, usingarray(instead of path as input). -
video_demo.py: inference YOLO-World on videos. -
inference.ipynb: jupyter notebook for YOLO-World. - Google Colab Notebook: We sincerely thank Onuralp for sharing the Colab Demo, you can have a try 😊！
We sincerely thank mmyolo, mmdetection, GLIP, and transformers for providing their wonderful code to the community!
If you find YOLO-World is useful in your research or applications, please consider giving us a star 🌟 and citing it.
@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}
}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.