yolo-world官方代码，预测 + 训练

资源内容介绍

YOLO（You Only Look Once）是一种在计算机视觉和机器学习领域广受欢迎的目标检测系统。YOLO模型以其高效性、速度和准确性在实时目标检测任务中表现突出，被广泛应用于自动驾驶、监控系统、视频分析等领域。YOLO的核心思想是在单一神经网络中直接预测边界框和概率，这与传统的目标检测方法不同，后者通常采用多步骤流程，例如选择性搜索选取候选区域，再对这些区域进行分类和边界框回归。YOLO模型的一个显著特点是它在检测速度和准确率之间的出色权衡。与基于区域的方法相比，YOLO在处理图像时速度更快，因为它只需要一个单一的神经网络评估，而不需要通过复杂的管道。这使得YOLO非常适合于对实时性要求高的应用。此外，YOLO对图像中的目标位置、大小和外观特征进行联合推理，从而提供更加精确的检测结果。YOLO模型自2015年首次提出以来，已经发展出多个版本，如YOLOv2、YOLOv3、YOLOv4，以及YOLOv5和最新的YOLOv7，每一个新版本都在前一版本的基础上进行了改进，增强了模型的性能。改进的方面包括但不限于网络结构的创新、锚框策略的优化、损失函数的调整、数据增强技术的使用等。每一代YOLO模型的更新都进一步提高了模型在各类数据集上的检测精度，同时也在不断减少对计算资源的需求，使得YOLO能够运行在更多类型的设备上。YOLO官方代码库通常包含用于训练模型的完整流程和用于预测的代码。这意味着开发者可以直接使用这些代码来训练自己的目标检测模型，或者用已经训练好的模型进行目标检测。官方代码库通常支持多种后端，如Darknet、PyTorch、TensorFlow等，为不同背景的开发者提供了灵活性。同时，代码库中还包含数据预处理、模型配置、训练脚本、评估脚本、预测脚本和可视化工具等，构成了一个完整的生态系统。从文件名称列表中我们仅看到了"YOLO-World"，这暗示该压缩包可能包含与YOLO相关的代码、文档和可能的模型文件。开发者通过这些资源可以深入了解YOLO模型的工作原理，也可以根据自己的需求进行模型的微调或者应用开发。YOLO社区提供了大量的预训练模型和开源资源，极大促进了目标检测技术在各个领域的应用。对于希望利用YOLO进行目标检测研究或者应用开发的研究者和工程师来说，官方代码库是一个宝贵的资源。它不仅提供了一个强大的目标检测框架，还通过开放的代码和活跃的社区支持，鼓励了技术创新和知识分享。通过阅读和使用官方代码，开发者可以更快地融入这一领域的最新进展，为自己的项目带来最先进的技术解决方案。

<div align="center"><img src="./assets/yolo_logo.png"><br><a href="https://scholar.google.com/citations?hl=zh-CN&user=PH8rJHYAAAAJ">Tianheng Cheng</a>^{<span>2,3,*</span>}, <a href="https://linsong.info/">Lin Song</a>^{<span>1,📧,*</span>},<a href="https://yxgeee.github.io/">Yixiao Ge</a>^{<span>1,🌟,2</span>},<a href="http://eic.hust.edu.cn/professor/liuwenyu/"> Wenyu Liu</a>^{<span>3</span>},<a href="https://xwcv.github.io/">Xinggang Wang</a>^{<span>3,📧</span>},<a href="https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en">Ying Shan</a>^{<span>1,2</span>}</br>\* Equal contribution 🌟 Project lead 📧 Corresponding author¹ Tencent AI Lab, ² ARC Lab, Tencent PCG³ Huazhong University of Science and Technology<br><div>[](https://wondervictor.github.io/)[](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>[](https://huggingface.co/spaces/stevengrove/YOLO-World)[](https://replicate.com/zsxkib/yolo-world)[](https://huggingface.co/papers/2401.17270)[](LICENSE)[](https://huggingface.co/spaces/SkalskiP/YOLO-World)[](https://supervision.roboflow.com/develop/notebooks/zero-shot-object-detection-with-yolo-world)[](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`.## 🔥 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) 🪄, 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. ## TODOYOLO-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