<div align="center">
<img src="./assets/yolo_logo.png" width=60%>
<br>
<a href="https://scholar.google.com/citations?hl=zh-CN&user=PH8rJHYAAAAJ">Tianheng Cheng</a><sup><span>2,3,*</span></sup>,
<a href="https://linsong.info/">Lin Song</a><sup><span>1,ð§,*</span></sup>,
<a href="https://yxgeee.github.io/">Yixiao Ge</a><sup><span>1,ð,2</span></sup>,
<a href="http://eic.hust.edu.cn/professor/liuwenyu/"> Wenyu Liu</a><sup><span>3</span></sup>,
<a href="https://xwcv.github.io/">Xinggang Wang</a><sup><span>3,ð§</span></sup>,
<a href="https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en">Ying Shan</a><sup><span>1,2</span></sup>
</br>
\* Equal contribution ð Project lead ð§ Corresponding author
<sup>1</sup> Tencent AI Lab, <sup>2</sup> ARC Lab, Tencent PCG
<sup>3</sup> Huazhong University of Science and Technology
<br>
<div>
[![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)
<a href="https://colab.research.google.com/github/AILab-CVC/YOLO-World/blob/master/inference.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
[![demo](https://img.shields.io/badge/ð¤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/ð¤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-ð¤Spaces-orange)](https://huggingface.co/spaces/SkalskiP/YOLO-World)
[![yologuide](https://img.shields.io/badge/ðNotebook-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/)
</div>
</div>
## 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`.
## ð¥ Updates
`[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) ðª, and it's better for fine-tuning on custom datasets and improves the training/inference efficiency ð!
`[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 [ð¤ 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 ð¤](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 âï¸!
If you have suggestionsð or ideasð¡,**we would love for you to bring them up in the [Roadmap](https://github.com/AILab-CVC/YOLO-World/issues/109)** â¤ï¸!
> YOLO-World ç®åæ£å¨ç§¯æå¼åä¸ðï¼å¦æä½ æ建议æè
æ³æ³ð¡ï¼**æ们é常å¸ææ¨å¨ [Roadmap](https://github.com/AILab-CVC/YOLO-World/issues/109) ä¸æåºæ¥** â¤ï¸ï¼
## [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.
> æ们å¨GitHubçdiscussionä¸å»ºç«äºå
³äºYOLO-Worldç常è§é®çï¼è¿éå°æ¶é
没有合适的资源?快使用搜索试试~ 我知道了~
[CVPR 2024] 实时开放词汇对象检测.zip
共166个文件
py:113个
md:20个
txt:12个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 4 浏览量
2024-11-26
12:42:59
上传
评论
收藏 2.35MB ZIP 举报
温馨提示
[CVPR 2024] 实时开放词汇对象检测程天恒2,3,* , 林松1,,* , 葛一笑1,,2 , 刘文宇3 , 王兴刚3, , 山英1,2* 同等贡献 项目负责人 通讯作者1腾讯人工智能实验室 2腾讯PCG ARC实验室 3华中科技大学 注意YOLO-World 仍在积极开发中!我们建议大家使用英语来交流问题,因为这有助于来自世界各地的开发人员一起讨论、分享经验和回答问题。如需了解营业许可和其他相关咨询,请随时联系yixiaoge@tencent.com。 更新[2024-11-5]: We update the YOLO-World-Image and you can try it at HuggingFace YOLO-World-Image (Preview Version). It's a preview version and we are still improving it! Detailed documents about training and few-shot inference are com
资源推荐
资源详情
资源评论
收起资源包目录
[CVPR 2024] 实时开放词汇对象检测.zip (166个子文件)
nvdsparsebbox_mmyolo.cpp 4KB
Dockerfile 1KB
.dockerignore 15B
.gitattributes 696B
.gitignore 1KB
.gitmodules 108B
inference.ipynb 1009KB
bus.jpg 476KB
zidane.jpg 165KB
lvis_v1_class_texts.json 28KB
lvis_v1_base_class_captions.json 20KB
obj365v1_class_texts.json 5KB
coco_class_texts.json 1022B
LICENSE 69KB
README.md 20KB
model_convert.md 6KB
data.md 5KB
README.md 4KB
README.md 4KB
finetuning.md 4KB
prompt_yolo_world.md 3KB
reparameterize.md 3KB
README.md 3KB
tflite_deploy.md 2KB
deploy.md 2KB
README.md 2KB
README.md 2KB
README_zh-CN.md 2KB
installation.md 1KB
updates.md 731B
READEME.md 534B
faq.md 512B
README.md 464B
README_zh-CN.md 406B
finetune_yoloworld.png 466KB
yolo_arch.png 298KB
yolo_logo.png 100KB
reparameterize.png 63KB
mm_mix_img_transforms.py 46KB
yolo_world_head.py 29KB
yolo_bricks.py 24KB
yolo_world_seg_head.py 24KB
image_prompt_demo.py 12KB
yolo_world_image.py 11KB
numpy_coder.py 11KB
yolo_world_pafpn.py 10KB
gradio_demo.py 9KB
yolo_world_v2_seg_l_vlpan_bn_2e-4_80e_8gpus_seghead_finetune_lvis.py 9KB
yolo_world_v2_seg_m_vlpan_bn_2e-4_80e_8gpus_seghead_finetune_lvis.py 9KB
yolo_world_seg_l_dual_vlpan_2e-4_80e_8gpus_seghead_finetune_lvis.py 9KB
yolo_world_seg_m_dual_vlpan_2e-4_80e_8gpus_seghead_finetune_lvis.py 9KB
yolo_world.py 9KB
model.py 9KB
ort_nms.py 9KB
yolo_world_seg_l_dual_vlpan_2e-4_80e_8gpus_allmodules_finetune_lvis.py 9KB
yolow_v5_optim_constructor.py 8KB
yolo_world_seg_m_dual_vlpan_2e-4_80e_8gpus_allmodules_finetune_lvis.py 8KB
mm_backbone.py 8KB
tflite_demo.py 8KB
trt_nms.py 8KB
image_demo.py 8KB
yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 8KB
onnx_demo.py 8KB
yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 7KB
yolo_world_v2_m_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 7KB
yolo_world_v2_l_clip_large_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_800ft_lvis_minival.py 7KB
yolo_world_v2_s_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py 7KB
yolov5_mixed_grounding.py 7KB
yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_cc3mlite_train_lvis_minival.py 7KB
yolo_world_v2_xl_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 7KB
export_onnx.py 7KB
yolo_world_v2_xl_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolov5_cc3m_grounding.py 7KB
yolo_world_v2_m_vlpan_bn_noeinsum_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_prompt_tuning_coco.py 7KB
yolo_world_v2_l_vlpan_bn_sgd_1e-3_80e_8gpus_mask-refine_finetune_coco.py 7KB
yolo_world_l_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_x_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_s_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_m_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_v2_l_clip_large_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_v2_m_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_l_dual_vlpan_l2norm_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_val.py 7KB
backendwrapper.py 7KB
yolo_world_v2_l_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_val.py 7KB
yolo_world_v2_s_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py 7KB
yolo_world_v2_l_vlpan_bn_sgd_1e-3_40e_8gpus_finetune_coco.py 7KB
yolo_world_l_efficient_neck_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_l_dual_vlpan_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_v2_s_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_v2_x_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_v2_l_efficient_neck_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_v2_m_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_v2_s_rep_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_v2_s_bn_2e-4_80e_8gpus_mask-refine_finetune_coco.py 6KB
yolo_world_l_dual_vlpan_2e-4_80e_8gpus_finetune_coco.py 6KB
yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_image_prompt_demo.py 5KB
共 166 条
- 1
- 2
资源评论
徐浪老师
- 粉丝: 8253
- 资源: 1万+
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功