人工智能领域近年来发展迅猛，其应用已经渗透到了社会生活的各个方面。随着技术的进步，对于大量数据的处理和分析需求也日益增长。数据标注作为机器学习流程中的重要步骤，其质量和效率直接影响到最终模型的性能。为了提升标注工作的效率，AI自动标注数据辅助工具应运而生。
X-AnyLabeling是一款专门设计用于简化和加速数据标注过程的工具。它的出现，极大地改变了数据处理的工作方式，尤其是在图像、语音、文本等不同类型数据的标注上。X-AnyLabeling的核心优势在于其能够利用AI技术，通过导入预先训练好的模型，自动执行数据标注任务。这不仅提高了标注工作的速度，还大幅度降低了人工标注的成本和错误率。
该工具不仅适用于已经拥有模型的用户，即使是初学者或者没有足够资源进行大规模模型训练的开发者，也可以利用X-AnyLabeling来尝试AI标注技术。用户可以通过上传自己的数据集，使用X-AnyLabeling中已经集成的先进模型进行标注，从而快速获得标注后的数据，用于后续的机器学习模型训练和验证。
在实际应用中，X-AnyLabeling表现出色，尤其在图像识别和分类领域。开发者可以通过简单配置，将深度学习算法中的图像识别模型嵌入到X-AnyLabeling中，使得工具能够自动识别图像中的对象，并进行标记。例如，在自动驾驶车辆的研究中，需要大量标注道路、行人、车辆等图像数据，使用X-AnyLabeling可以大大提高标注效率。
此外，X-AnyLabeling的设计充分考虑了用户体验，其界面友好，操作简便，即使是不具备深厚技术背景的用户也能够快速上手。其通过图形化界面展示数据和标注结果，使得整个标注过程直观易懂。同时，它还支持多用户协同工作，团队成员可以在同一平台上共享数据集和标注结果，促进了团队合作和知识共享。
X-AnyLabeling的推出，不仅是技术进步的体现，更是人工智能辅助工具发展的一个重要里程碑。它不仅提升了数据处理的效率，还为AI技术的普及和应用开辟了新的道路。随着AI技术的不断发展，X-AnyLabeling也会不断地更新迭代，引入更多先进的功能和算法，满足用户日益增长的需求。
X-AnyLabeling作为一款AI自动标注数据辅助工具，为数据标注工作提供了一个高效、准确的解决方案。它的应用范围广泛，不仅能够加速数据处理流程，还能够显著提高数据质量，为机器学习和人工智能的研究与应用提供了有力支持。
<div align="center">
<p>
<a href="https://github.com/CVHub520/X-AnyLabeling/" target="_blank">
<img alt="X-AnyLabeling" height="200px" src="https://github.com/user-attachments/assets/0714a182-92bd-4b47-b48d-1c5d7c225176"></a>
</p>
[English](README.md) | [ç®ä½ä¸æ](README_zh-CN.md)
</div>
<p align="center">
<a href="./LICENSE"><img src="https://img.shields.io/badge/License-LGPL v3-blue.svg"></a>
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<img src="https://github.com/user-attachments/assets/0b1e3c69-a800-4497-9bad-4332c1ce1ebf" width="100%">
<div align="center"><strong>Segment Anything v2</strong></div>
</br>
| **Tracking by HBB Detection** | **Tracking by OBB Detection** |
| :---: | :---: |
| <img src="https://github.com/user-attachments/assets/be67d4f8-eb31-4bb3-887c-d954bb4a5d6d" width="100%"> | <img src="https://github.com/user-attachments/assets/d85b1102-124a-4971-9332-c51fd2b1c47b" width="100%"> |
| **Tracking by Instance Segmentation** | **Tracking by Pose Estimation** |
| <img src="https://github.com/user-attachments/assets/8d412dc6-62c7-4bb2-9a1e-026448acf2bf" width="100%"> | <img src="https://github.com/user-attachments/assets/bab038a7-3023-4097-bdcc-90e5009477c0" width="100%"> |
## 𥳠What's New
- Sep. 2024:
- Release version [2.4.2](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.4.2)
- ð§¸ð§¸ð§¸ Added support for image matting based on [RMBG v1.4 model](https://huggingface.co/briaai/RMBG-1.4).
- ð¥ð¥ð¥ Added support for interactive video object tracking based on [Segment-Anything-2](https://github.com/CVHub520/segment-anything-2). [[Tutorial](examples/interactive_video_object_segmentation/README.md)]
<br>
<details>
<summary>Click to view more news.</summary>
- Aug. 2024:
- Release version [2.4.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.4.1)
- Support [tracking-by-det/obb/seg/pose](./examples/multiple_object_tracking/README.md) tasks.
- Support [Segment-Anything-2](https://github.com/facebookresearch/segment-anything-2) model! (Recommended)
- Support [Grounding-SAM2](./docs/en/model_zoo.md) model.
- Support lightweight model for Japanese recognition.
- Jul. 2024:
- Add PPOCR-Recognition and KIE import/export functionality for training PP-OCR task.
- Add ODVG import/export functionality for training grounding task.
- Add support to annotate KIE linking field.
- Support [RT-DETRv2](https://github.com/lyuwenyu/RT-DETR) model.
- Support [Depth Anything v2](https://github.com/DepthAnything/Depth-Anything-V2) model.
- Jun. 2024:
- Support [YOLOv8-Pose](https://docs.ultralytics.com/tasks/pose/) model.
- Add [yolo-pose](./docs/en/user_guide.md) import/export functionality.
- May. 2024:
- Support [YOLOv8-World](https://docs.ultralytics.com/models/yolo-world), [YOLOv8-oiv7](https://docs.ultralytics.com/models/yolov8), [YOLOv10](https://github.com/THU-MIG/yolov10) model.
- Release version [2.3.6](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.6).
- Add feature to display confidence score.
- Mar. 2024:
- Release version [2.3.5](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.5).
- Feb. 2024:
- Release version [2.3.4](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.4).
- Enable label display feature.
- Release version [2.3.3](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.3).
- Release version [2.3.2](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.2).
- Support [YOLOv9](https://github.com/WongKinYiu/yolov9) model.
- Support the conversion from a horizontal bounding box to a rotated bounding box.
- Supports label deletion and renaming. For more details, please refer to the [document](./docs/zh_cn/user_guide.md).
- Support for quick tag correction is available; please refer to this [document](./docs/en/user_guide.md) for guidance.
- Release version [2.3.1](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.1).
- Jan. 2024:
- Combining CLIP and SAM models for enhanced semantic and spatial understanding. An example can be found [here](./anylabeling/configs/auto_labeling/edge_sam_with_chinese_clip.yaml).
- Add support for the [Depth Anything](https://github.com/LiheYoung/Depth-Anything.git) model in the depth estimation task.
- Release version [2.3.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.3.0).
- Support [YOLOv8-OBB](https://github.com/ultralytics/ultralytics) model.
- Support [RTMDet](https://github.com/open-mmlab/mmyolo/tree/main/configs/rtmdet) and [RTMO](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose) model.
- Release a [chinese license plate](https://github.com/we0091234/Chinese_license_plate_detection_recognition) detection and recognition model based on YOLOv5.
- Dec. 2023:
- Release version [2.2.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.2.0).
- Support [EdgeSAM](https://github.com/chongzhou96/EdgeSAM) to optimize for efficient execution on edge devices with minimal performance compromise.
- Support YOLOv5-Cls and YOLOv8-Cls model.
- Nov. 2023:
- Release version [2.1.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.1.0).
- Support [InternImage](https://arxiv.org/abs/2211.05778) model (**CVPR'23**).
- Release version [2.0.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v2.0.0).
- Added support for Grounding-SAM, combining [GroundingDINO](https://github.com/wenyi5608/GroundingDINO) with [HQ-SAM](https://github.com/SysCV/sam-hq) to achieve sota zero-shot high-quality predictions!
- Enhanced support for [HQ-SAM](https://github.com/SysCV/sam-hq) model to achieve high-quality mask predictions.
- Support the [PersonAttribute](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/en/PULC/PULC_person_attribute_en.md) and [VehicleAttribute](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.5/docs/en/PULC/PULC_vehicle_attribute_en.md) model for multi-label classification task.
- Introducing a new multi-label attribute annotation functionality.
- Release version [1.1.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v1.1.0).
- Support pose estimation: [YOLOv8-Pose](https://github.com/ultralytics/ultralytics).
- Support object-level tag with yolov5_ram.
- Add a new feature enabling batch labeling for arbitrary unknown categories based on Grounding-DINO.
- Oct. 2023:
- Release version [1.0.0](https://github.com/CVHub520/X-AnyLabeling/releases/tag/v1.0.0).
- Add a new feature for rotation box.
- Support [YOLOv5-OBB](https://github.com/hukaixuan19970627/yolov5_obb) with [DroneVehicle](https://github.com/VisDrone/DroneVehicle) and [DOTA](https://captain-whu.github.io/DOTA/index.html)-v1.0/v1.5/v2.0 model.
- SOTA Zero-Shot Object Detection - [GroundingDINO](https://github.com/wenyi5608/GroundingDINO) is released.
- SOTA Image Tagging Model - [Recognize Anything](https://github.com/xinyu1205/Tag2Text) is released.
- Support YOLOv5-SAM and YOLOv8-EfficientViT_SAM union task.
- Support YOLOv5 and YOLOv8 segmentation task.
- Release [Gold-YOLO](https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO) and [DAMO-YOLO](https://github.com/tinyvision/DAMO-YOLO) models.
- Release MOT algorithms: [OC_Sort](https://github.com/noahcao/OC_SORT) (**CVPR'23**).
- Add a new feature for small object detection using [SAHI](https://github.com/obss/sahi).
- Sep. 2023:
- Release version [0. 100+评论
不擅长编程的程序员X-AnyLabeling-main.zip 43.26MB