Update README.md

README.md

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+---
+title: YOLOv12 Demo
+emoji: 🚀
+colorFrom: indigo
+colorTo: blue
+sdk: gradio  # 或者 streamlit
+sdk_version: "4.44.1"  # 这里可以指定 Gradio/Streamlit 版本
+app_file: app.py  # 确保这个文件是你的 Gradio/Streamlit 入口文件
+pinned: false
+---
 
 
-<div align="center">
-<h1>YOLOv12</h1>
-<h3>YOLOv12: Attention-Centric Real-Time Object Detectors</h3>
-
-[Yunjie Tian](https://sunsmarterjie.github.io/)<sup>1</sup>, [Qixiang Ye](https://people.ucas.ac.cn/~qxye?language=en)<sup>2</sup>, [David Doermann](https://cse.buffalo.edu/~doermann/)<sup>1</sup>
-
-<sup>1</sup>  University at Buffalo, SUNY, <sup>2</sup> University of Chinese Academy of Sciences.
-
-
-<p align="center">
-  <img src="assets/tradeoff.svg" width=90%> <br>
-  Comparison with popular methods in terms of latency-accuracy (left) and FLOPs-accuracy (right) trade-offs
-</p>
-
-</div>
-
-[![arXiv](https://img.shields.io/badge/arXiv-2502.12524-b31b1b.svg)](https://arxiv.org/abs/2502.12524)
-
-## Updates
-- 2025/02/19: [arXiv version](https://arxiv.org/abs/2502.12524) is public.
-
-
-<details>
-  <summary>
-  <font size="+1">Abstract</font>
-  </summary>
-Enhancing the network architecture of the YOLO framework has been crucial for a long time but has focused on CNN-based improvements despite the proven superiority of attention mechanisms in modeling capabilities. This is because attention-based models cannot match the speed of CNN-based models. This paper proposes an attention-centric YOLO framework, namely YOLOv12, that matches the speed of previous CNN-based ones while harnessing the performance benefits of attention mechanisms.
-
-YOLOv12 surpasses all popular real-time object detectors in accuracy with competitive speed. For example, YOLOv12-N achieves 40.6% mAP with an inference latency of 1.64 ms on a T4 GPU, outperforming advanced YOLOv10-N / YOLOv11-N by 2.1%/1.2% mAP with a comparable speed. This advantage extends to other model scales. YOLOv12 also surpasses end-to-end real-time detectors that improve DETR, such as RT-DETR / RT-DETRv2: YOLOv12-S beats RT-DETR-R18 / RT-DETRv2-R18 while running 42% faster, using only 36% of the computation and 45% of the parameters.
-</details>
-
-
-## Main Results
-COCO
-
-| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>T4 TensorRT10<br> | params<br><sup>(M) | FLOPs<br><sup>(G) |
-| :----------------------------------------------------------------------------------- | :-------------------: | :-------------------:| :------------------------------:| :-----------------:| :---------------:|
-| [YOLO12n](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12n.pt) | 640                   | 40.6                 | 1.64                            | 2.6                | 6.5               |
-| [YOLO12s](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12s.pt) | 640                   | 48.0                 | 2.61                            | 9.3                | 21.4              |
-| [YOLO12m](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12m.pt) | 640                   | 52.5                 | 4.86                            | 20.2               | 67.5              |
-| [YOLO12l](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12l.pt) | 640                   | 53.7                 | 6.77                            | 26.4               | 88.9              |
-| [YOLO12x](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12x.pt) | 640                   | 55.2                 | 11.79                           | 59.1               | 199.0             |
-
-## Installation
-```
-wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
-conda create -n yolov12 python=3.11
-conda activate yolov12
-pip install -r requirements.txt
-pip install -e .
-```
-
-## Validation
-[`yolov12n`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12n.pt)
-[`yolov12s`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12s.pt)
-[`yolov12m`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12m.pt)
-[`yolov12l`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12l.pt)
-[`yolov12x`](https://github.com/sunsmarterjie/yolov12/releases/download/v1.0/yolov12x.pt)
-
-```python
-from ultralytics import YOLO
-
-model = YOLO('yolov12{n/s/m/l/x}.pt')
-model.val(data='coco.yaml', save_json=True)
-```
-
-## Training 
-```python
-from ultralytics import YOLO
-
-model = YOLO('yolov12n.yaml')
-
-# Train the model
-results = model.train(
-  data='coco.yaml',
-  epochs=600, 
-  batch=256, 
-  imgsz=640,
-  scale=0.5,  # S:0.9; M:0.9; L:0.9; X:0.9
-  mosaic=1.0,
-  mixup=0.0,  # S:0.05; M:0.15; L:0.15; X:0.2
-  copy_paste=0.1,  # S:0.15; M:0.4; L:0.5; X:0.6
-  device="0,1,2,3",
-)
-
-# Evaluate model performance on the validation set
-metrics = model.val()
-
-# Perform object detection on an image
-results = model("path/to/image.jpg")
-results[0].show()
-
-```
-
-## Prediction
-```python
-from ultralytics import YOLO
-
-model = YOLO('yolov12{n/s/m/l/x}.pt')
-model.predict()
-```
-
-## Export
-```python
-from ultralytics import YOLO
-
-model = YOLO('yolov12{n/s/m/l/x}.pt')
-model.export(format="engine", half=True)  # or format="onnx"
-```
-
-
-## Demo
-
-```
-python app.py
-# Please visit http://127.0.0.1:7860
-```
-
-
-## Acknowledgement
-
-The code is based on [ultralytics](https://github.com/ultralytics/ultralytics). Thanks for their excellent work!
-
-## Citation
-
-```BibTeX
-@article{tian2025yolov12,
-  title={YOLOv12: Attention-Centric Real-Time Object Detectors},
-  author={Tian, Yunjie and Ye, Qixiang and Doermann, David},
-  journal={arXiv preprint arXiv:2502.12524},
-  year={2025}
-}
-```
-