Add Chinese Modes and Tasks Docs (#6274)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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docs/en/hub/app/android.md
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---
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comments: true
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description: Learn about the Ultralytics Android App, enabling real-time object detection using YOLO models. Discover in-app features, quantization methods, and delegate options for optimal performance.
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keywords: Ultralytics, Android App, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, CPU, GPU, Hexagon, NNAPI
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---
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# Ultralytics Android App: Real-time Object Detection with YOLO Models
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://user-images.githubusercontent.com/26833433/281124469-6b3b0945-dbb1-44c8-80a9-ef6bc778b299.jpg" alt="Ultralytics HUB preview image"></a>
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<br>
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<div align="center">
|
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<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
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||||
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
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<br>
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<br>
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<a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
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<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>
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</div>
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The Ultralytics Android App is a powerful tool that allows you to run YOLO models directly on your Android device for real-time object detection. This app utilizes TensorFlow Lite for model optimization and various hardware delegates for acceleration, enabling fast and efficient object detection.
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## Quantization and Acceleration
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To achieve real-time performance on your Android device, YOLO models are quantized to either FP16 or INT8 precision. Quantization is a process that reduces the numerical precision of the model's weights and biases, thus reducing the model's size and the amount of computation required. This results in faster inference times without significantly affecting the model's accuracy.
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### FP16 Quantization
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FP16 (or half-precision) quantization converts the model's 32-bit floating-point numbers to 16-bit floating-point numbers. This reduces the model's size by half and speeds up the inference process, while maintaining a good balance between accuracy and performance.
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### INT8 Quantization
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INT8 (or 8-bit integer) quantization further reduces the model's size and computation requirements by converting its 32-bit floating-point numbers to 8-bit integers. This quantization method can result in a significant speedup, but it may lead to a slight reduction in mean average precision (mAP) due to the lower numerical precision.
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!!! tip "mAP Reduction in INT8 Models"
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The reduced numerical precision in INT8 models can lead to some loss of information during the quantization process, which may result in a slight decrease in mAP. However, this trade-off is often acceptable considering the substantial performance gains offered by INT8 quantization.
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## Delegates and Performance Variability
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Different delegates are available on Android devices to accelerate model inference. These delegates include CPU, [GPU](https://www.tensorflow.org/lite/android/delegates/gpu), [Hexagon](https://www.tensorflow.org/lite/android/delegates/hexagon) and [NNAPI](https://www.tensorflow.org/lite/android/delegates/nnapi). The performance of these delegates varies depending on the device's hardware vendor, product line, and specific chipsets used in the device.
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1. **CPU**: The default option, with reasonable performance on most devices.
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2. **GPU**: Utilizes the device's GPU for faster inference. It can provide a significant performance boost on devices with powerful GPUs.
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3. **Hexagon**: Leverages Qualcomm's Hexagon DSP for faster and more efficient processing. This option is available on devices with Qualcomm Snapdragon processors.
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4. **NNAPI**: The Android Neural Networks API (NNAPI) serves as an abstraction layer for running ML models on Android devices. NNAPI can utilize various hardware accelerators, such as CPU, GPU, and dedicated AI chips (e.g., Google's Edge TPU, or the Pixel Neural Core).
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Here's a table showing the primary vendors, their product lines, popular devices, and supported delegates:
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| Vendor | Product Lines | Popular Devices | Delegates Supported |
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|-----------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|
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| [Qualcomm](https://www.qualcomm.com/) | [Snapdragon (e.g., 800 series)](https://www.qualcomm.com/snapdragon) | [Samsung Galaxy S21](https://www.samsung.com/global/galaxy/galaxy-s21-5g/), [OnePlus 9](https://www.oneplus.com/9), [Google Pixel 6](https://store.google.com/product/pixel_6) | CPU, GPU, Hexagon, NNAPI |
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| [Samsung](https://www.samsung.com/) | [Exynos (e.g., Exynos 2100)](https://www.samsung.com/semiconductor/minisite/exynos/) | [Samsung Galaxy S21 (Global version)](https://www.samsung.com/global/galaxy/galaxy-s21-5g/) | CPU, GPU, NNAPI |
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| [MediaTek](https://i.mediatek.com/) | [Dimensity (e.g., Dimensity 1200)](https://i.mediatek.com/dimensity-1200) | [Realme GT](https://www.realme.com/global/realme-gt), [Xiaomi Redmi Note](https://www.mi.com/en/phone/redmi/note-list) | CPU, GPU, NNAPI |
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| [HiSilicon](https://www.hisilicon.com/) | [Kirin (e.g., Kirin 990)](https://www.hisilicon.com/en/products/Kirin) | [Huawei P40 Pro](https://consumer.huawei.com/en/phones/p40-pro/), [Huawei Mate 30 Pro](https://consumer.huawei.com/en/phones/mate30-pro/) | CPU, GPU, NNAPI |
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| [NVIDIA](https://www.nvidia.com/) | [Tegra (e.g., Tegra X1)](https://developer.nvidia.com/content/tegra-x1) | [NVIDIA Shield TV](https://www.nvidia.com/en-us/shield/shield-tv/), [Nintendo Switch](https://www.nintendo.com/switch/) | CPU, GPU, NNAPI |
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Please note that the list of devices mentioned is not exhaustive and may vary depending on the specific chipsets and device models. Always test your models on your target devices to ensure compatibility and optimal performance.
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Keep in mind that the choice of delegate can affect performance and model compatibility. For example, some models may not work with certain delegates, or a delegate may not be available on a specific device. As such, it's essential to test your model and the chosen delegate on your target devices for the best results.
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## Getting Started with the Ultralytics Android App
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To get started with the Ultralytics Android App, follow these steps:
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1. Download the Ultralytics App from the [Google Play Store](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app).
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2. Launch the app on your Android device and sign in with your Ultralytics account. If you don't have an account yet, create one [here](https://hub.ultralytics.com/).
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3. Once signed in, you will see a list of your trained YOLO models. Select a model to use for object detection.
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4. Grant the app permission to access your device's camera.
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5. Point your device's camera at objects you want to detect. The app will display bounding boxes and class labels in real-time as it detects objects.
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6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
|
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With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
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48
docs/en/hub/app/index.md
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docs/en/hub/app/index.md
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---
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comments: true
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description: Explore the Ultralytics HUB App, offering the ability to run YOLOv5 and YOLOv8 models on your iOS and Android devices with optimized performance.
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keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object detection, image recognition, mobile device, hardware acceleration, Apple Neural Engine, Android GPU, NNAPI, custom model training
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---
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# Ultralytics HUB App
|
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
|
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<br>
|
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<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
|
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<br>
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<br>
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<a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
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<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a>
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<a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
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<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>
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</div>
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Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mobile app that allows you to run YOLOv5 and YOLOv8 models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) and [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) devices. With the HUB App, you can utilize hardware acceleration features like Apple's Neural Engine (ANE) or Android GPU and Neural Network API (NNAPI) delegates to achieve impressive performance on your mobile device.
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## Features
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- **Run YOLOv5 and YOLOv8 models**: Experience the power of YOLO models on your mobile device for real-time object detection and image recognition tasks.
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- **Hardware Acceleration**: Benefit from Apple ANE on iOS devices or Android GPU and NNAPI delegates for optimized performance.
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- **Custom Model Training**: Train custom models with the Ultralytics HUB platform and preview them live using the HUB App.
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- **Mobile Compatibility**: The HUB App supports both iOS and Android devices, bringing the power of YOLO models to a wide range of users.
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## App Documentation
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- [**iOS**](./ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
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- [**Android**](./android.md): Explore TFLite acceleration on Android mobile devices.
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Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
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docs/en/hub/app/ios.md
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docs/en/hub/app/ios.md
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---
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comments: true
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description: Execute object detection in real-time on your iOS devices utilizing YOLO models. Leverage the power of the Apple Neural Engine and Core ML for fast and efficient object detection.
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keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple Neural Engine, Core ML, FP16, INT8, quantization
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---
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# Ultralytics iOS App: Real-time Object Detection with YOLO Models
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|
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
|
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<img width="100%" src="https://user-images.githubusercontent.com/26833433/281124469-6b3b0945-dbb1-44c8-80a9-ef6bc778b299.jpg" alt="Ultralytics HUB preview image"></a>
|
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<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
|
||||
<br>
|
||||
<br>
|
||||
<a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
|
||||
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a>
|
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</div>
|
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|
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The Ultralytics iOS App is a powerful tool that allows you to run YOLO models directly on your iPhone or iPad for real-time object detection. This app utilizes the Apple Neural Engine and Core ML for model optimization and acceleration, enabling fast and efficient object detection.
|
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## Quantization and Acceleration
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To achieve real-time performance on your iOS device, YOLO models are quantized to either FP16 or INT8 precision. Quantization is a process that reduces the numerical precision of the model's weights and biases, thus reducing the model's size and the amount of computation required. This results in faster inference times without significantly affecting the model's accuracy.
|
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|
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### FP16 Quantization
|
||||
|
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FP16 (or half-precision) quantization converts the model's 32-bit floating-point numbers to 16-bit floating-point numbers. This reduces the model's size by half and speeds up the inference process, while maintaining a good balance between accuracy and performance.
|
||||
|
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### INT8 Quantization
|
||||
|
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INT8 (or 8-bit integer) quantization further reduces the model's size and computation requirements by converting its 32-bit floating-point numbers to 8-bit integers. This quantization method can result in a significant speedup, but it may lead to a slight reduction in accuracy.
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## Apple Neural Engine
|
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|
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The Apple Neural Engine (ANE) is a dedicated hardware component integrated into Apple's A-series and M-series chips. It's designed to accelerate machine learning tasks, particularly for neural networks, allowing for faster and more efficient execution of your YOLO models.
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By combining quantized YOLO models with the Apple Neural Engine, the Ultralytics iOS App achieves real-time object detection on your iOS device without compromising on accuracy or performance.
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| Release Year | iPhone Name | Chipset Name | Node Size | ANE TOPs |
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|--------------|------------------------------------------------------|-------------------------------------------------------|-----------|----------|
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| 2017 | [iPhone X](https://en.wikipedia.org/wiki/IPhone_X) | [A11 Bionic](https://en.wikipedia.org/wiki/Apple_A11) | 10 nm | 0.6 |
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| 2018 | [iPhone XS](https://en.wikipedia.org/wiki/IPhone_XS) | [A12 Bionic](https://en.wikipedia.org/wiki/Apple_A12) | 7 nm | 5 |
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| 2019 | [iPhone 11](https://en.wikipedia.org/wiki/IPhone_11) | [A13 Bionic](https://en.wikipedia.org/wiki/Apple_A13) | 7 nm | 6 |
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| 2020 | [iPhone 12](https://en.wikipedia.org/wiki/IPhone_12) | [A14 Bionic](https://en.wikipedia.org/wiki/Apple_A14) | 5 nm | 11 |
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| 2021 | [iPhone 13](https://en.wikipedia.org/wiki/IPhone_13) | [A15 Bionic](https://en.wikipedia.org/wiki/Apple_A15) | 5 nm | 15.8 |
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| 2022 | [iPhone 14](https://en.wikipedia.org/wiki/IPhone_14) | [A16 Bionic](https://en.wikipedia.org/wiki/Apple_A16) | 4 nm | 17.0 |
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Please note that this list only includes iPhone models from 2017 onwards, and the ANE TOPs values are approximate.
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## Getting Started with the Ultralytics iOS App
|
||||
|
||||
To get started with the Ultralytics iOS App, follow these steps:
|
||||
|
||||
1. Download the Ultralytics App from the [App Store](https://apps.apple.com/xk/app/ultralytics/id1583935240).
|
||||
|
||||
2. Launch the app on your iOS device and sign in with your Ultralytics account. If you don't have an account yet, create one [here](https://hub.ultralytics.com/).
|
||||
|
||||
3. Once signed in, you will see a list of your trained YOLO models. Select a model to use for object detection.
|
||||
|
||||
4. Grant the app permission to access your device's camera.
|
||||
|
||||
5. Point your device's camera at objects you want to detect. The app will display bounding boxes and class labels in real-time as it detects objects.
|
||||
|
||||
6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
|
||||
|
||||
With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
|
||||
159
docs/en/hub/datasets.md
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159
docs/en/hub/datasets.md
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|
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@ -0,0 +1,159 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how Ultralytics HUB datasets streamline your ML workflow. Upload, format, validate, access, share, edit or delete datasets for Ultralytics YOLO model training.
|
||||
keywords: Ultralytics, HUB datasets, YOLO model training, upload datasets, dataset validation, ML workflow, share datasets
|
||||
---
|
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# HUB Datasets
|
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|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) datasets are a practical solution for managing and leveraging your custom datasets.
|
||||
|
||||
Once uploaded, datasets can be immediately utilized for model training. This integrated approach facilitates a seamless transition from dataset management to model training, significantly simplifying the entire process.
|
||||
|
||||
## Upload Dataset
|
||||
|
||||
Ultralytics HUB datasets are just like YOLOv5 and YOLOv8 🚀 datasets. They use the same structure and the same label formats to keep everything simple.
|
||||
|
||||
Before you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML file inside the dataset root directory** and that **your dataset YAML, directory and ZIP have the same name**, as shown in the example below, and then zip the dataset directory.
|
||||
|
||||
For example, if your dataset is called "coco8", as our [COCO8](https://docs.ultralytics.com/datasets/detect/coco8) example dataset, then you should have a `coco8.yaml` inside your `coco8/` directory, which will create a `coco8.zip` when zipped:
|
||||
|
||||
```bash
|
||||
zip -r coco8.zip coco8
|
||||
```
|
||||
|
||||
You can download our [COCO8](https://github.com/ultralytics/hub/blob/main/example_datasets/coco8.zip) example dataset and unzip it to see exactly how to structure your dataset.
|
||||
|
||||
<p align="center">
|
||||
<img src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_1.jpg" alt="COCO8 Dataset Structure" width="80%" />
|
||||
</p>
|
||||
|
||||
The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format.
|
||||
|
||||
!!! example "coco8.yaml"
|
||||
|
||||
```yaml
|
||||
--8<-- "ultralytics/cfg/datasets/coco8.yaml"
|
||||
```
|
||||
|
||||
After zipping your dataset, you should validate it before uploading it to Ultralytics HUB. Ultralytics HUB conducts the dataset validation check post-upload, so by ensuring your dataset is correctly formatted and error-free ahead of time, you can forestall any setbacks due to dataset rejection.
|
||||
|
||||
```py
|
||||
from ultralytics.hub import check_dataset
|
||||
|
||||
check_dataset('path/to/coco8.zip')
|
||||
```
|
||||
|
||||
Once your dataset ZIP is ready, navigate to the [Datasets](https://hub.ultralytics.com/datasets) page by clicking on the **Datasets** button in the sidebar.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also upload a dataset directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
Click on the **Upload Dataset** button on the top right of the page. This action will trigger the **Upload Dataset** dialog.
|
||||
|
||||
|
||||
|
||||
Upload your dataset in the _Dataset .zip file_ field.
|
||||
|
||||
You have the additional option to set a custom name and description for your Ultralytics HUB dataset.
|
||||
|
||||
When you're happy with your dataset configuration, click **Upload**.
|
||||
|
||||
|
||||
|
||||
After your dataset is uploaded and processed, you will be able to access it from the Datasets page.
|
||||
|
||||
|
||||
|
||||
You can view the images in your dataset grouped by splits (Train, Validation, Test).
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
Each image can be enlarged for better visualization.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Also, you can analyze your dataset by click on the **Overview** tab.
|
||||
|
||||
|
||||
|
||||
Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) on your dataset.
|
||||
|
||||
|
||||
|
||||
## Share Dataset
|
||||
|
||||
!!! info "Info"
|
||||
|
||||
Ultralytics HUB's sharing functionality provides a convenient way to share datasets with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
|
||||
|
||||
??? note "Note"
|
||||
|
||||
You have control over the general access of your datasets.
|
||||
|
||||
You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the dataset, regardless of whether they have an Ultralytics HUB account or not.
|
||||
|
||||
Navigate to the Dataset page of the dataset you want to share, open the dataset actions dropdown and click on the **Share** option. This action will trigger the **Share Dataset** dialog.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also share a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
|
||||
|
||||
|
||||
|
||||
Set the general access to "Unlisted" and click **Save**.
|
||||
|
||||
|
||||
|
||||
Now, anyone who has the direct link to your dataset can view it.
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can easily click on the dataset's link shown in the **Share Dataset** dialog to copy it.
|
||||
|
||||
|
||||
|
||||
## Edit Dataset
|
||||
|
||||
Navigate to the Dataset page of the dataset you want to edit, open the dataset actions dropdown and click on the **Edit** option. This action will trigger the **Update Dataset** dialog.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also edit a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
|
||||
|
||||
|
||||
|
||||
Apply the desired modifications to your dataset and then confirm the changes by clicking **Save**.
|
||||
|
||||
|
||||
|
||||
## Delete Dataset
|
||||
|
||||
Navigate to the Dataset page of the dataset you want to delete, open the dataset actions dropdown and click on the **Delete** option. This action will delete the dataset.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also delete a dataset directly from the [Datasets](https://hub.ultralytics.com/datasets) page.
|
||||
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
|
||||
|
||||
|
||||
61
docs/en/hub/index.md
Normal file
61
docs/en/hub/index.md
Normal file
|
|
@ -0,0 +1,61 @@
|
|||
---
|
||||
comments: true
|
||||
description: Gain insights into training and deploying your YOLOv5 and YOLOv8 models with Ultralytics HUB. Explore pre-trained models, templates and various integrations.
|
||||
keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pretrained models, model integrations
|
||||
---
|
||||
|
||||
# Ultralytics HUB
|
||||
|
||||
<a href="https://bit.ly/ultralytics_hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
|
||||
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
|
||||
<br>
|
||||
<br>
|
||||
<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
|
||||
<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb">
|
||||
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
</div>
|
||||
|
||||
👋 Hello from the [Ultralytics](https://ultralytics.com/) Team! We've been working hard these last few months to launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new web tool for training and deploying all your YOLOv5 and YOLOv8 🚀 models from one spot!
|
||||
|
||||
## Introduction
|
||||
|
||||
HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to easily upload their data and train new models quickly. It offers a range of pre-trained models and templates to choose from, making it easy for users to get started with training their own models. Once a model is trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
|
||||
</p>
|
||||
|
||||
We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://ultralytics.com/discord">Discord</a> community for questions and discussions!
|
||||
|
||||
- [**Quickstart**](./quickstart.md). Start training and deploying YOLO models with HUB in seconds.
|
||||
- [**Datasets: Preparing and Uploading**](./datasets.md). Learn how to prepare and upload your datasets to HUB in YOLO format.
|
||||
- [**Projects: Creating and Managing**](./projects.md). Group your models into projects for improved organization.
|
||||
- [**Models: Training and Exporting**](./models.md). Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
|
||||
- [**Integrations: Options**](./integrations.md). Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
|
||||
- [**Ultralytics HUB App**](./app/index.md). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
|
||||
* [**iOS**](./app/ios.md). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
|
||||
* [**Android**](./app/android.md). Explore TFLite acceleration on mobile devices.
|
||||
- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
|
||||
458
docs/en/hub/inference_api.md
Normal file
458
docs/en/hub/inference_api.md
Normal file
|
|
@ -0,0 +1,458 @@
|
|||
---
|
||||
comments: true
|
||||
description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or CLI for swift object detection.
|
||||
keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, CLI, Quickstart
|
||||
---
|
||||
|
||||
# YOLO Inference API
|
||||
|
||||
The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
|
||||
|
||||
|
||||
Screenshot of the Inference API section in the trained model Preview tab.
|
||||
|
||||
## API URL
|
||||
|
||||
The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:
|
||||
|
||||
```
|
||||
https://api.ultralytics.com/v1/predict
|
||||
```
|
||||
|
||||
## Example Usage in Python
|
||||
|
||||
To access the YOLO Inference API with the specified model and API key using Python, you can use the following code:
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `path/to/image.jpg` with the path to the image you want to analyze.
|
||||
|
||||
## Example Usage with CLI
|
||||
|
||||
You can use the YOLO Inference API with the command-line interface (CLI) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
|
||||
|
||||
```bash
|
||||
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
||||
-H "x-api-key: API_KEY" \
|
||||
-F "image=@/path/to/image.jpg" \
|
||||
-F "size=640" \
|
||||
-F "confidence=0.25" \
|
||||
-F "iou=0.45"
|
||||
```
|
||||
|
||||
## Passing Arguments
|
||||
|
||||
This command sends a POST request to the YOLO Inference API with the specified `MODEL_ID` in the URL and the `API_KEY` in the request `headers`, along with the image file specified by `@path/to/image.jpg`.
|
||||
|
||||
Here's an example of passing the `size`, `confidence`, and `iou` arguments via the API URL using the `requests` library in Python:
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
In this example, the `data` dictionary contains the query arguments `size`, `confidence`, and `iou`, which tells the API to run inference at image size 640 with confidence and IoU thresholds of 0.25 and 0.45.
|
||||
|
||||
This will send the query parameters along with the file in the POST request. See the table below for a full list of available inference arguments.
|
||||
|
||||
| Inference Argument | Default | Type | Notes |
|
||||
|--------------------|---------|---------|------------------------------------------------|
|
||||
| `size` | `640` | `int` | valid range is `32` - `1280` pixels |
|
||||
| `confidence` | `0.25` | `float` | valid range is `0.01` - `1.0` |
|
||||
| `iou` | `0.45` | `float` | valid range is `0.0` - `0.95` |
|
||||
| `url` | `''` | `str` | optional image URL if not image file is passed |
|
||||
| `normalize` | `False` | `bool` | |
|
||||
|
||||
## Return JSON format
|
||||
|
||||
The YOLO Inference API returns a JSON list with the detection results. The format of the JSON list will be the same as the one produced locally by the `results[0].tojson()` command.
|
||||
|
||||
The JSON list contains information about the detected objects, their coordinates, classes, and confidence scores.
|
||||
|
||||
### Detect Model Format
|
||||
|
||||
YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
|
||||
|
||||
!!! example "Detect Model JSON Response"
|
||||
|
||||
=== "Local"
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load model
|
||||
model = YOLO('yolov8n.pt')
|
||||
|
||||
# Run inference
|
||||
results = model('image.jpg')
|
||||
|
||||
# Print image.jpg results in JSON format
|
||||
print(results[0].tojson())
|
||||
```
|
||||
|
||||
=== "CLI API"
|
||||
```bash
|
||||
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
||||
-H "x-api-key: API_KEY" \
|
||||
-F "image=@/path/to/image.jpg" \
|
||||
-F "size=640" \
|
||||
-F "confidence=0.25" \
|
||||
-F "iou=0.45"
|
||||
```
|
||||
|
||||
=== "Python API"
|
||||
```python
|
||||
import requests
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
=== "JSON Response"
|
||||
```json
|
||||
{
|
||||
"success": True,
|
||||
"message": "Inference complete.",
|
||||
"data": [
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8359682559967041,
|
||||
"box": {
|
||||
"x1": 0.08974208831787109,
|
||||
"y1": 0.27418340047200523,
|
||||
"x2": 0.8706787109375,
|
||||
"y2": 0.9887352837456598
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8189555406570435,
|
||||
"box": {
|
||||
"x1": 0.5847355842590332,
|
||||
"y1": 0.05813225640190972,
|
||||
"x2": 0.8930277824401855,
|
||||
"y2": 0.9903111775716146
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "tie",
|
||||
"class": 27,
|
||||
"confidence": 0.2909725308418274,
|
||||
"box": {
|
||||
"x1": 0.3433395862579346,
|
||||
"y1": 0.6070465511745877,
|
||||
"x2": 0.40964522361755373,
|
||||
"y2": 0.9849439832899306
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Segment Model Format
|
||||
|
||||
YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
|
||||
|
||||
!!! example "Segment Model JSON Response"
|
||||
|
||||
=== "Local"
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load model
|
||||
model = YOLO('yolov8n-seg.pt')
|
||||
|
||||
# Run inference
|
||||
results = model('image.jpg')
|
||||
|
||||
# Print image.jpg results in JSON format
|
||||
print(results[0].tojson())
|
||||
```
|
||||
|
||||
=== "CLI API"
|
||||
```bash
|
||||
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
||||
-H "x-api-key: API_KEY" \
|
||||
-F "image=@/path/to/image.jpg" \
|
||||
-F "size=640" \
|
||||
-F "confidence=0.25" \
|
||||
-F "iou=0.45"
|
||||
```
|
||||
|
||||
=== "Python API"
|
||||
```python
|
||||
import requests
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
=== "JSON Response"
|
||||
Note `segments` `x` and `y` lengths may vary from one object to another. Larger or more complex objects may have more segment points.
|
||||
```json
|
||||
{
|
||||
"success": True,
|
||||
"message": "Inference complete.",
|
||||
"data": [
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.856913149356842,
|
||||
"box": {
|
||||
"x1": 0.1064866065979004,
|
||||
"y1": 0.2798851860894097,
|
||||
"x2": 0.8738358497619629,
|
||||
"y2": 0.9894873725043403
|
||||
},
|
||||
"segments": {
|
||||
"x": [
|
||||
0.421875,
|
||||
0.4203124940395355,
|
||||
0.41718751192092896
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.2888889014720917,
|
||||
0.2916666567325592,
|
||||
0.2916666567325592
|
||||
...
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8512625694274902,
|
||||
"box": {
|
||||
"x1": 0.5757311820983887,
|
||||
"y1": 0.053943040635850696,
|
||||
"x2": 0.8960096359252929,
|
||||
"y2": 0.985154045952691
|
||||
},
|
||||
"segments": {
|
||||
"x": [
|
||||
0.7515624761581421,
|
||||
0.75,
|
||||
0.7437499761581421
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.0555555559694767,
|
||||
0.05833333358168602,
|
||||
0.05833333358168602
|
||||
...
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "tie",
|
||||
"class": 27,
|
||||
"confidence": 0.6485961675643921,
|
||||
"box": {
|
||||
"x1": 0.33911995887756347,
|
||||
"y1": 0.6057066175672743,
|
||||
"x2": 0.4081430912017822,
|
||||
"y2": 0.9916408962673611
|
||||
},
|
||||
"segments": {
|
||||
"x": [
|
||||
0.37187498807907104,
|
||||
0.37031251192092896,
|
||||
0.3687500059604645
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.6111111044883728,
|
||||
0.6138888597488403,
|
||||
0.6138888597488403
|
||||
...
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Pose Model Format
|
||||
|
||||
YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
|
||||
|
||||
!!! example "Pose Model JSON Response"
|
||||
|
||||
=== "Local"
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
# Load model
|
||||
model = YOLO('yolov8n-seg.pt')
|
||||
|
||||
# Run inference
|
||||
results = model('image.jpg')
|
||||
|
||||
# Print image.jpg results in JSON format
|
||||
print(results[0].tojson())
|
||||
```
|
||||
|
||||
=== "CLI API"
|
||||
```bash
|
||||
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
|
||||
-H "x-api-key: API_KEY" \
|
||||
-F "image=@/path/to/image.jpg" \
|
||||
-F "size=640" \
|
||||
-F "confidence=0.25" \
|
||||
-F "iou=0.45"
|
||||
```
|
||||
|
||||
=== "Python API"
|
||||
```python
|
||||
import requests
|
||||
|
||||
# API URL, use actual MODEL_ID
|
||||
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
|
||||
|
||||
# Headers, use actual API_KEY
|
||||
headers = {"x-api-key": "API_KEY"}
|
||||
|
||||
# Inference arguments (optional)
|
||||
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
|
||||
|
||||
# Load image and send request
|
||||
with open("path/to/image.jpg", "rb") as image_file:
|
||||
files = {"image": image_file}
|
||||
response = requests.post(url, headers=headers, files=files, data=data)
|
||||
|
||||
print(response.json())
|
||||
```
|
||||
|
||||
=== "JSON Response"
|
||||
Note COCO-keypoints pretrained models will have 17 human keypoints. The `visible` part of the keypoints indicates whether a keypoint is visible or obscured. Obscured keypoints may be outside the image or may not be visible, i.e. a person's eyes facing away from the camera.
|
||||
```json
|
||||
{
|
||||
"success": True,
|
||||
"message": "Inference complete.",
|
||||
"data": [
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.8439509868621826,
|
||||
"box": {
|
||||
"x1": 0.1125,
|
||||
"y1": 0.28194444444444444,
|
||||
"x2": 0.7953125,
|
||||
"y2": 0.9902777777777778
|
||||
},
|
||||
"keypoints": {
|
||||
"x": [
|
||||
0.5058594942092896,
|
||||
0.5103894472122192,
|
||||
0.4920862317085266
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.48964157700538635,
|
||||
0.4643048942089081,
|
||||
0.4465252459049225
|
||||
...
|
||||
],
|
||||
"visible": [
|
||||
0.8726999163627625,
|
||||
0.653947651386261,
|
||||
0.9130823612213135
|
||||
...
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "person",
|
||||
"class": 0,
|
||||
"confidence": 0.7474289536476135,
|
||||
"box": {
|
||||
"x1": 0.58125,
|
||||
"y1": 0.0625,
|
||||
"x2": 0.8859375,
|
||||
"y2": 0.9888888888888889
|
||||
},
|
||||
"keypoints": {
|
||||
"x": [
|
||||
0.778544008731842,
|
||||
0.7976160049438477,
|
||||
0.7530890107154846
|
||||
...
|
||||
],
|
||||
"y": [
|
||||
0.27595141530036926,
|
||||
0.2378823608160019,
|
||||
0.23644638061523438
|
||||
...
|
||||
],
|
||||
"visible": [
|
||||
0.8900790810585022,
|
||||
0.789978563785553,
|
||||
0.8974530100822449
|
||||
...
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
62
docs/en/hub/integrations.md
Normal file
62
docs/en/hub/integrations.md
Normal file
|
|
@ -0,0 +1,62 @@
|
|||
---
|
||||
comments: true
|
||||
description: Explore integration options for Ultralytics HUB. Currently featuring Roboflow for dataset integration and multiple export formats for your trained models.
|
||||
keywords: Ultralytics HUB, Integrations, Roboflow, Dataset, Export, YOLOv5, YOLOv8, ONNX, CoreML, TensorRT, TensorFlow
|
||||
---
|
||||
|
||||
# HUB Integrations
|
||||
|
||||
🚧 **Under Construction** 🚧
|
||||
|
||||
Welcome to the Integrations guide for [Ultralytics HUB](https://hub.ultralytics.com/)! We are in the process of expanding this section to provide you with comprehensive guidance on integrating your YOLOv5 and YOLOv8 models with various platforms and formats. Currently, Roboflow is our available dataset integration, with a wide range of export integrations for your trained models.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
|
||||
</p>
|
||||
|
||||
## Available Integrations
|
||||
|
||||
### Dataset Integrations
|
||||
|
||||
- **Roboflow**: Seamlessly import your datasets for training.
|
||||
|
||||
### Export Integrations
|
||||
|
||||
| Format | `format` Argument | Model | Metadata | Arguments |
|
||||
|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|
|
||||
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
|
||||
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
|
||||
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
|
||||
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
|
||||
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
|
||||
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
|
||||
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
|
||||
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
|
||||
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
|
||||
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
|
||||
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
|
||||
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
|
||||
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |
|
||||
|
||||
## Coming Soon
|
||||
|
||||
- Additional Dataset Integrations
|
||||
- Detailed Export Integration Guides
|
||||
- Step-by-Step Tutorials for Each Integration
|
||||
|
||||
## Need Immediate Assistance?
|
||||
|
||||
While we're in the process of creating detailed guides:
|
||||
|
||||
- Browse through other [HUB Docs](https://docs.ultralytics.com/hub/) for detailed guides and tutorials.
|
||||
- Raise an issue on our [GitHub](https://github.com/ultralytics/hub/) for technical support.
|
||||
- Join our [Discord Community](https://ultralytics.com/discord/) for live discussions and community support.
|
||||
|
||||
We appreciate your patience as we work to make this section comprehensive and user-friendly. Stay tuned for updates!
|
||||
213
docs/en/hub/models.md
Normal file
213
docs/en/hub/models.md
Normal file
|
|
@ -0,0 +1,213 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to use Ultralytics HUB models for efficient and user-friendly AI model training. For easy model creation, training, evaluation and deployment, follow our detailed guide.
|
||||
keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
|
||||
---
|
||||
|
||||
# Ultralytics HUB Models
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) models provide a streamlined solution for training vision AI models on your custom datasets.
|
||||
|
||||
The process is user-friendly and efficient, involving a simple three-step creation and accelerated training powered by Utralytics YOLOv8. During training, real-time updates on model metrics are available so that you can monitor each step of the progress. Once training is completed, you can preview your model and easily deploy it to real-world applications. Therefore, Ultralytics HUB offers a comprehensive yet straightforward system for model creation, training, evaluation, and deployment.
|
||||
|
||||
## Train Model
|
||||
|
||||
Navigate to the [Models](https://hub.ultralytics.com/models) page by clicking on the **Models** button in the sidebar.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also train a model directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
Click on the **Train Model** button on the top right of the page. This action will trigger the **Train Model** dialog.
|
||||
|
||||
|
||||
|
||||
The **Train Model** dialog has three simple steps, explained below.
|
||||
|
||||
### 1. Dataset
|
||||
|
||||
In this step, you have to select the dataset you want to train your model on. After you selected a dataset, click **Continue**.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can skip this step if you train a model directly from the Dataset page.
|
||||
|
||||
|
||||
|
||||
### 2. Model
|
||||
|
||||
In this step, you have to choose the project in which you want to create your model, the name of your model and your model's architecture.
|
||||
|
||||
??? note "Note"
|
||||
|
||||
Ultralytics HUB will try to pre-select the project.
|
||||
|
||||
If you opened the **Train Model** dialog as described above, Ultralytics HUB will pre-select the last project you used.
|
||||
|
||||
If you opened the **Train Model** dialog from the Project page, Ultralytics HUB will pre-select the project you were inside of.
|
||||
|
||||
|
||||
|
||||
In case you don't have a project created yet, you can set the name of your project in this step and it will be created together with your model.
|
||||
|
||||
|
||||
|
||||
!!! info "Info"
|
||||
|
||||
You can read more about the available [YOLOv8](https://docs.ultralytics.com/models/yolov8) (and [YOLOv5](https://docs.ultralytics.com/models/yolov5)) architectures in our documentation.
|
||||
|
||||
When you're happy with your model configuration, click **Continue**.
|
||||
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
By default, your model will use a pre-trained model (trained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset) to reduce training time.
|
||||
|
||||
You can change this behaviour by opening the **Advanced Options** accordion.
|
||||
|
||||
### 3. Train
|
||||
|
||||
In this step, you will start training you model.
|
||||
|
||||
Ultralytics HUB offers three training options:
|
||||
|
||||
- Ultralytics Cloud **(COMING SOON)**
|
||||
- Google Colab
|
||||
- Bring your own agent
|
||||
|
||||
In order to start training your model, follow the instructions presented in this step.
|
||||
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
When you are on this step, before the training starts, you can change the default training configuration by opening the **Advanced Options** accordion.
|
||||
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
When you are on this step, you have the option to close the **Train Model** dialog and start training your model from the Model page later.
|
||||
|
||||
|
||||
|
||||
To start training your model using Google Colab, simply follow the instructions shown above or on the Google Colab notebook.
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
|
||||
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
|
||||
</a>
|
||||
|
||||
When the training starts, you can click **Done** and monitor the training progress on the Model page.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
In case the training stops and a checkpoint was saved, you can resume training your model from the Model page.
|
||||
|
||||
|
||||
|
||||
## Preview Model
|
||||
|
||||
Ultralytics HUB offers a variety of ways to preview your trained model.
|
||||
|
||||
You can preview your model if you click on the **Preview** tab and upload an image in the **Test** card.
|
||||
|
||||
|
||||
|
||||
You can also use our Ultralytics Cloud API to effortlessly [run inference](https://docs.ultralytics.com/hub/inference_api) with your custom model.
|
||||
|
||||
|
||||
|
||||
Furthermore, you can preview your model in real-time directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by [downloading](https://ultralytics.com/app_install) our [Ultralytics HUB Mobile Application](./app/index.md).
|
||||
|
||||
|
||||
|
||||
## Deploy Model
|
||||
|
||||
You can export your model to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle and many others.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can customize the export options of each format if you open the export actions dropdown and click on the **Advanced** option.
|
||||
|
||||
|
||||
|
||||
## Share Model
|
||||
|
||||
!!! info "Info"
|
||||
|
||||
Ultralytics HUB's sharing functionality provides a convenient way to share models with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
|
||||
|
||||
??? note "Note"
|
||||
|
||||
You have control over the general access of your models.
|
||||
|
||||
You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the model, regardless of whether they have an Ultralytics HUB account or not.
|
||||
|
||||
Navigate to the Model page of the model you want to share, open the model actions dropdown and click on the **Share** option. This action will trigger the **Share Model** dialog.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also share a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
|
||||
|
||||
|
||||
|
||||
Set the general access to "Unlisted" and click **Save**.
|
||||
|
||||
|
||||
|
||||
Now, anyone who has the direct link to your model can view it.
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can easily click on the model's link shown in the **Share Model** dialog to copy it.
|
||||
|
||||
|
||||
|
||||
## Edit Model
|
||||
|
||||
Navigate to the Model page of the model you want to edit, open the model actions dropdown and click on the **Edit** option. This action will trigger the **Update Model** dialog.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also edit a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
|
||||
|
||||
|
||||
|
||||
Apply the desired modifications to your model and then confirm the changes by clicking **Save**.
|
||||
|
||||
|
||||
|
||||
## Delete Model
|
||||
|
||||
Navigate to the Model page of the model you want to delete, open the model actions dropdown and click on the **Delete** option. This action will delete the model.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also delete a model directly from the [Models](https://hub.ultralytics.com/models) page or from the Project page of the project where your model is located.
|
||||
|
||||
|
||||
|
||||
??? note "Note"
|
||||
|
||||
If you change your mind, you can restore the model from the [Trash](https://hub.ultralytics.com/trash) page.
|
||||
|
||||
|
||||
169
docs/en/hub/projects.md
Normal file
169
docs/en/hub/projects.md
Normal file
|
|
@ -0,0 +1,169 @@
|
|||
---
|
||||
comments: true
|
||||
description: Learn how to manage Ultralytics HUB projects. Understand effective strategies to create, share, edit, delete, and compare models in an organized workspace.
|
||||
keywords: Ultralytics, HUB projects, Create project, Edit project, Share project, Delete project, Compare Models, Model Management
|
||||
---
|
||||
|
||||
# Ultralytics HUB Projects
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) projects provide an effective solution for consolidating and managing your models. If you are working with several models that perform similar tasks or have related purposes, Ultralytics HUB projects allow you to group these models together.
|
||||
|
||||
This creates a unified and organized workspace that facilitates easier model management, comparison and development. Having similar models or various iterations together can facilitate rapid benchmarking, as you can compare their effectiveness. This can lead to faster, more insightful iterative development and refinement of your models.
|
||||
|
||||
## Create Project
|
||||
|
||||
Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also create a project directly from the [Home](https://hub.ultralytics.com/home) page.
|
||||
|
||||
|
||||
|
||||
Click on the **Create Project** button on the top right of the page. This action will trigger the **Create Project** dialog, opening up a suite of options for tailoring your project to your needs.
|
||||
|
||||
|
||||
|
||||
Type the name of your project in the _Project name_ field or keep the default name and finalize the project creation with a single click.
|
||||
|
||||
You have the additional option to enrich your project with a description and a unique image, enhancing its recognizability on the Projects page.
|
||||
|
||||
When you're happy with your project configuration, click **Create**.
|
||||
|
||||
|
||||
|
||||
After your project is created, you will be able to access it from the Projects page.
|
||||
|
||||
|
||||
|
||||
Next, [train a model](https://docs.ultralytics.com/hub/models/#train-model) inside your project.
|
||||
|
||||
|
||||
|
||||
## Share Project
|
||||
|
||||
!!! info "Info"
|
||||
|
||||
Ultralytics HUB's sharing functionality provides a convenient way to share projects with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
|
||||
|
||||
??? note "Note"
|
||||
|
||||
You have control over the general access of your projects.
|
||||
|
||||
You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the project, regardless of whether they have an Ultralytics HUB account or not.
|
||||
|
||||
Navigate to the Project page of the project you want to share, open the project actions dropdown and click on the **Share** option. This action will trigger the **Share Project** dialog.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also share a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
|
||||
|
||||
Set the general access to "Unlisted" and click **Save**.
|
||||
|
||||
|
||||
|
||||
!!! warning "Warning"
|
||||
|
||||
When changing the general access of a project, the general access of the models inside the project will be changed as well.
|
||||
|
||||
Now, anyone who has the direct link to your project can view it.
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can easily click on the project's link shown in the **Share Project** dialog to copy it.
|
||||
|
||||
|
||||
|
||||
## Edit Project
|
||||
|
||||
Navigate to the Project page of the project you want to edit, open the project actions dropdown and click on the **Edit** option. This action will trigger the **Update Project** dialog.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also edit a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
|
||||
|
||||
Apply the desired modifications to your project and then confirm the changes by clicking **Save**.
|
||||
|
||||
|
||||
|
||||
## Delete Project
|
||||
|
||||
Navigate to the Project page of the project you want to delete, open the project actions dropdown and click on the **Delete** option. This action will delete the project.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also delete a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
|
||||
|
||||
|
||||
|
||||
!!! warning "Warning"
|
||||
|
||||
When deleting a project, the models inside the project will be deleted as well.
|
||||
|
||||
??? note "Note"
|
||||
|
||||
If you change your mind, you can restore the project from the [Trash](https://hub.ultralytics.com/trash) page.
|
||||
|
||||
|
||||
|
||||
## Compare Models
|
||||
|
||||
Navigate to the Project page of the project where the models you want to compare are located. To use the model comparison feature, click on the **Charts** tab.
|
||||
|
||||
|
||||
|
||||
This will display all the relevant charts. Each chart corresponds to a different metric and contains the performance of each model for that metric. The models are represented by different colors and you can hover over each data point to get more information.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
Each chart can be enlarged for better visualization.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You have the flexibility to customize your view by selectively hiding certain models. This feature allows you to concentrate on the models of interest.
|
||||
|
||||
|
||||
|
||||
## Reorder Models
|
||||
|
||||
??? note "Note"
|
||||
|
||||
Ultralytics HUB's reordering functionality works only inside projects you own.
|
||||
|
||||
Navigate to the Project page of the project where the models you want to reorder are located. Click on the designated reorder icon of the model you want to move and drag it to the desired location.
|
||||
|
||||
|
||||
|
||||
## Transfer Models
|
||||
|
||||
Navigate to the Project page of the project where the model you want to mode is located, open the project actions dropdown and click on the **Transfer** option. This action will trigger the **Transfer Model** dialog.
|
||||
|
||||
|
||||
|
||||
??? tip "Tip"
|
||||
|
||||
You can also transfer a model directly from the [Models](https://hub.ultralytics.com/models) page.
|
||||
|
||||
|
||||
|
||||
Select the project you want to transfer the model to and click **Save**.
|
||||
|
||||
|
||||
52
docs/en/hub/quickstart.md
Normal file
52
docs/en/hub/quickstart.md
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
---
|
||||
comments: true
|
||||
description: Kickstart your journey with Ultralytics HUB. Learn how to train and deploy YOLOv5 and YOLOv8 models in seconds with our Quickstart guide.
|
||||
keywords: Ultralytics HUB, Quickstart, YOLOv5, YOLOv8, model training, quick deployment, drag-and-drop interface, real-time object detection
|
||||
---
|
||||
|
||||
# Quickstart Guide for Ultralytics HUB
|
||||
|
||||
🚧 **Under Construction** 🚧
|
||||
|
||||
Thank you for visiting the Quickstart guide for [Ultralytics HUB](https://hub.ultralytics.com/)! We're currently hard at work building out this page to provide you with step-by-step instructions on how to get up and running with HUB in no time.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
|
||||
title="YouTube video player" frameborder="0"
|
||||
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
|
||||
allowfullscreen>
|
||||
</iframe>
|
||||
<br>
|
||||
<strong>Watch:</strong> Train Your Custom YOLO Models In A Few Clicks with Ultralytics HUB.
|
||||
</p>
|
||||
|
||||
In the meantime, here's a brief overview of what you can expect from Ultralytics HUB:
|
||||
|
||||
## What is Ultralytics HUB?
|
||||
|
||||
Ultralytics HUB is your one-stop solution for training and deploying YOLOv5 and YOLOv8 models. It's designed with user experience in mind, featuring a drag-and-drop interface to make uploading data and training new models a breeze. Whether you're a beginner or an experienced machine learning practitioner, HUB has a range of pre-trained models and templates to accelerate your projects.
|
||||
|
||||
## Key Features
|
||||
|
||||
- **User-Friendly Interface**: Simply drag and drop your data to start training.
|
||||
- **Pre-Trained Models**: Choose from a selection of pre-trained models to kick-start your projects.
|
||||
- **Real-Time Object Detection**: Deploy trained models easily for real-time object detection, instance segmentation, and classification tasks.
|
||||
|
||||
## Coming Soon
|
||||
|
||||
- Detailed Steps to Start Your First Project
|
||||
- Guide on Preparing and Uploading Datasets
|
||||
- Tutorial on Model Training and Exporting
|
||||
- Integration Options and How-To's
|
||||
- And much more!
|
||||
|
||||
## Need Help Now?
|
||||
|
||||
While we're polishing this page, feel free to:
|
||||
|
||||
- Browse through other [HUB Docs](https://docs.ultralytics.com/hub/) for detailed guides and tutorials.
|
||||
- Raise an issue on our [GitHub](https://github.com/ultralytics/hub/) for technical support.
|
||||
- Join our [Discord Community](https://ultralytics.com/discord/) for live discussions and community support.
|
||||
|
||||
Stay tuned! We'll be back soon with more detailed information to help you get the most out of Ultralytics HUB. Thank you for your patience and interest!
|
||||
Loading…
Add table
Add a link
Reference in a new issue