Update README.md

README.md

@@ -1,302 +1,9 @@
-<div align="center">
-<p>
-   <a align="left" href="https://ultralytics.com/yolov5" target="_blank">
-   <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
-</p>
-
-English | [简体中文](.github/README_cn.md)
-<br>
-<div>
-   <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="CI CPU testing"></a>
-   <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
-   <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-   <br>
-   <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-   <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-   <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
-</div>
-
-<br>
-<p>
-YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
- open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
-</p>
-
-<div align="center">
-   <a href="https://github.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://www.linkedin.com/company/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://twitter.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://www.producthunt.com/@glenn_jocher">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://youtube.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://www.facebook.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://www.instagram.com/ultralytics/">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
-   </a>
-</div>
-
-<!--
-<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
-<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
--->
-
-</div>
-
-## <div align="center">Documentation</div>
-
-See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
-
-## <div align="center">Quick Start Examples</div>
-
-<details open>
-<summary>Install</summary>
-
-Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
-[**Python>=3.7.0**](https://www.python.org/) environment, including
-[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
-
-```bash
-git clone https://github.com/ultralytics/yolov5  # clone
-cd yolov5
-pip install -r requirements.txt  # install
-```
-
-</details>
-
-<details open>
-<summary>Inference</summary>
-
-YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
-YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
-
-```python
-import torch
-
-# Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom
-
-# Images
-img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
-
-# Inference
-results = model(img)
-
-# Results
-results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
-```
-
-</details>
-
-<details>
-<summary>Inference with detect.py</summary>
-
-`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
-the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
-
-```bash
-python detect.py --source 0  # webcam
-                          img.jpg  # image
-                          vid.mp4  # video
-                          path/  # directory
-                          path/*.jpg  # glob
-                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-```
-
-</details>
-
-<details>
-<summary>Training</summary>
-
-The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
-results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
-and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
-YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
-1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
-largest `--batch-size` possible, or pass `--batch-size -1` for
-YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
-
-```bash
-python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
-                                       yolov5s                                64
-                                       yolov5m                                40
-                                       yolov5l                                24
-                                       yolov5x                                16
-```
-
-<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
-
-</details>
-
-<details open>
-<summary>Tutorials</summary>
-
-- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED
-- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️
-  RECOMMENDED
-- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  🌟 NEW
-- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)  🌟 NEW
-- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
-- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  ⭐ NEW
-- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
-- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
-- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
-- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
-- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
-- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)  ⭐ NEW
-- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998)  ⭐ NEW
-
-</details>
-
-## <div align="center">Environments</div>
-
-Get started in seconds with our verified environments. Click each icon below for details.
-
-<div align="center">
-    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
-    </a>
-    <a href="https://www.kaggle.com/ultralytics/yolov5">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
-    </a>
-    <a href="https://hub.docker.com/r/ultralytics/yolov5">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
-    </a>
-    <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
-    </a>
-    <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
-    </a>
-</div>
-
-## <div align="center">Integrations</div>
-
-<div align="center">
-    <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
-    </a>
-    <a href="https://roboflow.com/?ref=ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
-    </a>
-</div>
-
-|Weights and Biases|Roboflow ⭐ NEW|
-|:-:|:-:|
-|Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
-
-<!-- ## <div align="center">Compete and Win</div>
-
-We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
-
-<p align="center">
-  <a href="https://github.com/ultralytics/yolov5/discussions/3213">
-  <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
-</p> -->
-
-## <div align="center">Why YOLOv5</div>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
-<details>
-  <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
-</details>
-<details>
-  <summary>Figure Notes (click to expand)</summary>
-
-- **COCO AP val** denotes [email protected]:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
-- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
-- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
-- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-
-</details>
-
-### Pretrained Checkpoints
-
-|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
-|---                    |---  |---    |---    |---    |---    |---    |---    |---
-|[YOLOv5n][assets]      |640  |28.0   |45.7   |**45** |**6.3**|**0.6**|**1.9**|**4.5**
-|[YOLOv5s][assets]      |640  |37.4   |56.8   |98     |6.4    |0.9    |7.2    |16.5
-|[YOLOv5m][assets]      |640  |45.4   |64.1   |224    |8.2    |1.7    |21.2   |49.0
-|[YOLOv5l][assets]      |640  |49.0   |67.3   |430    |10.1   |2.7    |46.5   |109.1
-|[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7
-|                       |     |       |       |       |       |       |       |
-|[YOLOv5n6][assets]     |1280 |36.0   |54.4   |153    |8.1    |2.1    |3.2    |4.6
-|[YOLOv5s6][assets]     |1280 |44.8   |63.7   |385    |8.2    |3.6    |12.6   |16.8
-|[YOLOv5m6][assets]     |1280 |51.3   |69.3   |887    |11.1   |6.8    |35.7   |50.0
-|[YOLOv5l6][assets]     |1280 |53.7   |71.3   |1784   |15.8   |10.5   |76.8   |111.4
-|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |55.0<br>**55.8** |72.7<br>**72.7** |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
-
-<details>
-  <summary>Table Notes (click to expand)</summary>
-
-- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
-- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
-- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
-
-</details>
-
-## <div align="center">Contribute</div>
-
-We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
-
-<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
-
-## <div align="center">Contact</div>
-
-For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
-professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
-
-<br>
-
-<div align="center">
-    <a href="https://github.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.linkedin.com/company/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://twitter.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.producthunt.com/@glenn_jocher">
-    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://youtube.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.facebook.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.instagram.com/ultralytics/">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
-    </a>
-</div>
-
-[assets]: https://github.com/ultralytics/yolov5/releases
-[tta]: https://github.com/ultralytics/yolov5/issues/303
+title: ValorantTracker
+emoji: 🔥
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+sdk_version: 5.15.0
+app_file: app.py
+pinned: false
+license: mit