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34
docs/en/hub/api/index.md
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docs/en/hub/api/index.md
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---
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||||
description: Discover what's next for Ultralytics with our under-construction page, previewing new, groundbreaking AI and ML features coming soon.
|
||||
keywords: Ultralytics, coming soon, under construction, new features, AI updates, ML advancements, YOLO, technology preview
|
||||
---
|
||||
|
||||
# Under Construction 🏗️🌟
|
||||
|
||||
Welcome to the Ultralytics "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you!
|
||||
|
||||
## Exciting New Features on the Way 🎉
|
||||
|
||||
- **Innovative Breakthroughs:** Get ready for advanced features and services that will transform your AI and ML experience.
|
||||
- **New Horizons:** Anticipate novel products that redefine AI and ML capabilities.
|
||||
- **Enhanced Services:** We're upgrading our services for greater efficiency and user-friendliness.
|
||||
|
||||
## Stay Updated 🚧
|
||||
|
||||
This placeholder page is your first stop for upcoming developments. Keep an eye out for:
|
||||
|
||||
- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news.
|
||||
- **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers.
|
||||
- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights.
|
||||
|
||||
## We Value Your Input 🗣️
|
||||
|
||||
Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/contact).
|
||||
|
||||
## Thank You, Community! 🌍
|
||||
|
||||
Your [contributions](https://docs.ultralytics.com/help/contributing) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
|
||||
|
||||
---
|
||||
|
||||
Excited for what's coming? Bookmark this page and get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖
|
||||
100
docs/en/hub/app/android.md
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docs/en/hub/app/android.md
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---
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comments: true
|
||||
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.
|
||||
keywords: Ultralytics, Android App, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, CPU, GPU, Hexagon, NNAPI
|
||||
---
|
||||
|
||||
# Ultralytics Android App: Real-time Object Detection with YOLO Models
|
||||
|
||||
<a href="https://bit.ly/ultralytics_hub" target="_blank">
|
||||
<img width="100%" src="https://user-images.githubusercontent.com/26833433/281124469-6b3b0945-dbb1-44c8-80a9-ef6bc778b299.jpg" 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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
|
||||
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>
|
||||
</div>
|
||||
|
||||
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.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/AIvrQ7y0aLo"
|
||||
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> Getting Started with the Ultralytics HUB App (IOS & Android)
|
||||
</p>
|
||||
|
||||
## Quantization and Acceleration
|
||||
|
||||
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.
|
||||
|
||||
### FP16 Quantization
|
||||
|
||||
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.
|
||||
|
||||
### INT8 Quantization
|
||||
|
||||
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.
|
||||
|
||||
!!! Tip "mAP Reduction in INT8 Models"
|
||||
|
||||
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.
|
||||
|
||||
## Delegates and Performance Variability
|
||||
|
||||
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.
|
||||
|
||||
1. **CPU**: The default option, with reasonable performance on most devices.
|
||||
2. **GPU**: Utilizes the device's GPU for faster inference. It can provide a significant performance boost on devices with powerful GPUs.
|
||||
3. **Hexagon**: Leverages Qualcomm's Hexagon DSP for faster and more efficient processing. This option is available on devices with Qualcomm Snapdragon processors.
|
||||
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).
|
||||
|
||||
Here's a table showing the primary vendors, their product lines, popular devices, and supported delegates:
|
||||
|
||||
| Vendor | Product Lines | Popular Devices | Delegates Supported |
|
||||
|-----------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|
|
||||
| [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 |
|
||||
| [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 |
|
||||
| [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 |
|
||||
| [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 |
|
||||
| [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 |
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
## Getting Started with the Ultralytics Android App
|
||||
|
||||
To get started with the Ultralytics Android App, follow these steps:
|
||||
|
||||
1. Download the Ultralytics App from the [Google Play Store](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app).
|
||||
|
||||
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/).
|
||||
|
||||
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 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.
|
||||
48
docs/en/hub/app/index.md
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docs/en/hub/app/index.md
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|
||||
---
|
||||
comments: true
|
||||
description: Explore the Ultralytics HUB App, offering the ability to run YOLOv5 and YOLOv8 models on your iOS and Android devices with optimized performance.
|
||||
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
|
||||
---
|
||||
|
||||
# Ultralytics HUB App
|
||||
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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="Apple App store"></a>
|
||||
<a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
|
||||
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>
|
||||
</div>
|
||||
|
||||
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.
|
||||
|
||||
## Features
|
||||
|
||||
- **Run YOLOv5 and YOLOv8 models**: Experience the power of YOLO models on your mobile device for real-time object detection and image recognition tasks.
|
||||
- **Hardware Acceleration**: Benefit from Apple ANE on iOS devices or Android GPU and NNAPI delegates for optimized performance.
|
||||
- **Custom Model Training**: Train custom models with the Ultralytics HUB platform and preview them live using the HUB App.
|
||||
- **Mobile Compatibility**: The HUB App supports both iOS and Android devices, bringing the power of YOLO models to a wide range of users.
|
||||
|
||||
## App Documentation
|
||||
|
||||
- [**iOS**](ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
|
||||
- [**Android**](android.md): Explore TFLite acceleration on Android mobile devices.
|
||||
|
||||
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.
|
||||
90
docs/en/hub/app/ios.md
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docs/en/hub/app/ios.md
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|
||||
---
|
||||
comments: true
|
||||
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.
|
||||
keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple Neural Engine, Core ML, FP16, INT8, quantization
|
||||
---
|
||||
|
||||
# Ultralytics iOS App: Real-time Object Detection with YOLO Models
|
||||
|
||||
<a href="https://bit.ly/ultralytics_hub" target="_blank">
|
||||
<img width="100%" src="https://user-images.githubusercontent.com/26833433/281124469-6b3b0945-dbb1-44c8-80a9-ef6bc778b299.jpg" 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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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="Apple App store"></a>
|
||||
</div>
|
||||
|
||||
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.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/AIvrQ7y0aLo"
|
||||
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> Getting Started with the Ultralytics HUB App (IOS & Android)
|
||||
</p>
|
||||
|
||||
## Quantization and Acceleration
|
||||
|
||||
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.
|
||||
|
||||
### FP16 Quantization
|
||||
|
||||
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.
|
||||
|
||||
### INT8 Quantization
|
||||
|
||||
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.
|
||||
|
||||
## Apple Neural Engine
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
| Release Year | iPhone Name | Chipset Name | Node Size | ANE TOPs |
|
||||
|--------------|------------------------------------------------------|-------------------------------------------------------|-----------|----------|
|
||||
| 2017 | [iPhone X](https://en.wikipedia.org/wiki/IPhone_X) | [A11 Bionic](https://en.wikipedia.org/wiki/Apple_A11) | 10 nm | 0.6 |
|
||||
| 2018 | [iPhone XS](https://en.wikipedia.org/wiki/IPhone_XS) | [A12 Bionic](https://en.wikipedia.org/wiki/Apple_A12) | 7 nm | 5 |
|
||||
| 2019 | [iPhone 11](https://en.wikipedia.org/wiki/IPhone_11) | [A13 Bionic](https://en.wikipedia.org/wiki/Apple_A13) | 7 nm | 6 |
|
||||
| 2020 | [iPhone 12](https://en.wikipedia.org/wiki/IPhone_12) | [A14 Bionic](https://en.wikipedia.org/wiki/Apple_A14) | 5 nm | 11 |
|
||||
| 2021 | [iPhone 13](https://en.wikipedia.org/wiki/IPhone_13) | [A15 Bionic](https://en.wikipedia.org/wiki/Apple_A15) | 5 nm | 15.8 |
|
||||
| 2022 | [iPhone 14](https://en.wikipedia.org/wiki/IPhone_14) | [A16 Bionic](https://en.wikipedia.org/wiki/Apple_A16) | 4 nm | 17.0 |
|
||||
|
||||
Please note that this list only includes iPhone models from 2017 onwards, and the ANE TOPs values are approximate.
|
||||
|
||||
## 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.
|
||||
89
docs/en/hub/cloud-training.md
Normal file
89
docs/en/hub/cloud-training.md
Normal file
@ -0,0 +1,89 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn how to use Ultralytics HUB for efficient and user-friendly AI model training in the cloud. Follow our detailed guide for easy model creation, training, evaluation, and deployment.
|
||||
keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
|
||||
---
|
||||
|
||||
# Cloud Training
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) provides a powerful and user-friendly cloud platform to train custom object detection models. Easily select your dataset and the desired training method, then kick off the process with just a few clicks. Ultralytics HUB offers pre-built options and various model architectures to streamline your workflow.
|
||||
|
||||
|
||||
|
||||
Read more about creating and other details of a Model at our [HUB Models page](models.md)
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ie3vLUDNYZo"
|
||||
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> New Feature 🌟 Introducing Ultralytics HUB Cloud Training
|
||||
</p>
|
||||
|
||||
## Selecting an Instance
|
||||
|
||||
For details on picking a model and instances for it, please read our [Instances guide Page](models.md)
|
||||
|
||||
## Steps to Train the Model
|
||||
|
||||
Once the instance has been selected, training a model using Ultralytics HUB is a three-step process, as below:
|
||||
|
||||
1. Picking a Dataset - Read more about datasets, steps to add/remove datasets from the [Dataset page](datasets.md)
|
||||
2. Picking a Model - Read more about models, steps to create/share and handle a model on the [HUB Models page](models.md)
|
||||
3. Training the Model on the Chosen Dataset
|
||||
|
||||
Ultralytics HUB offers three training options:
|
||||
|
||||
- **Ultralytics Cloud** - Explained in this page.
|
||||
- **Google Colab** - Train on Google's popular Colab notebooks.
|
||||
- **Bring your own agent** - Train models locally on your own hardware or on-premise GPU servers.
|
||||
|
||||
In order to start training your model, follow the instructions presented in these steps.
|
||||
|
||||
## Training via Ultralytics Cloud
|
||||
|
||||
To start training your model using Ultralytics Cloud, simply select the Training Duration, Available Instances, and Payment options.
|
||||
|
||||
**Training Duration** - Ultralytics offers two kinds of training durations:
|
||||
|
||||
1. Training based on `Epochs`: This option allows you to train your model based on the number of times your dataset needs to go through the cycle of train, label, and test. The exact pricing based on the number of epochs is hard to determine. Hence, if the credit gets exhausted before the intended number of epochs, the training pauses, and you get a prompt to top-up and resume training.
|
||||
2. Timed Training: The timed training feature allows you to fix the time duration of the entire training process and also determines the estimated amount before the start of training.
|
||||
|
||||
|
||||
|
||||
When the training starts, you can click **Done** and monitor the training progress on the Model page.
|
||||
|
||||
## Monitor Your Training
|
||||
|
||||
Once the model and mode of training have been selected, you can monitor the training procedure on the `Train` section with the link provided in the terminal (on your agent/Google Colab) or a button from Ultralytics Cloud.
|
||||
|
||||
|
||||
|
||||
## Stopping and Resuming Your Training
|
||||
|
||||
Once the training has started, you can `Stop` the training, which will also correspondingly pause the credit usage. You can then `Resume` the training from the point where it stopped.
|
||||
|
||||
|
||||
|
||||
## Payments and Billing Options
|
||||
|
||||
Ultralytics HUB offers `Pay Now` as upfront and/or using `Ultralytics HUB Account` as a wallet to top up and fulfill the billing. You can choose from two types of accounts: `Free` and `Pro` user.
|
||||
|
||||
To access your profile, click on the profile picture in the bottom left corner.
|
||||
|
||||
|
||||
|
||||
Click on the Billing tab to view your current plan and options to upgrade it.
|
||||
|
||||
|
||||
|
||||
You will be prompted with different available plans, and you can pick from the available plans as shown below.
|
||||
|
||||
|
||||
|
||||
Navigate to the Payment page, fill in the details, and complete the payment.
|
||||
|
||||
|
||||
146
docs/en/hub/datasets.md
Normal file
146
docs/en/hub/datasets.md
Normal file
@ -0,0 +1,146 @@
|
||||
---
|
||||
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
|
||||
---
|
||||
|
||||
# HUB Datasets
|
||||
|
||||
[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.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/R42s2zFtNIY"
|
||||
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> Watch: Upload Datasets to Ultralytics HUB | Complete Walkthrough of Dataset Upload Feature
|
||||
</p>
|
||||
|
||||
## 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.
|
||||
|
||||
|
||||
|
||||
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 / Delete 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.
|
||||
|
||||
|
||||
|
||||
Apply the desired modifications to your dataset and then confirm the changes by clicking **Save**.
|
||||
|
||||
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.
|
||||
|
||||
!!! 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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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%" alt="space">
|
||||
<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 loading="lazy" 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.
|
||||
480
docs/en/hub/inference-api.md
Normal file
480
docs/en/hub/inference-api.md
Normal file
@ -0,0 +1,480 @@
|
||||
---
|
||||
comments: true
|
||||
description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or cURL for swift object detection.
|
||||
keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, cURL, 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.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/OpWpBI35A5Y"
|
||||
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> Ultralytics HUB Inference API Walkthrough
|
||||
</p>
|
||||
|
||||
## 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 cURL
|
||||
|
||||
You can use the YOLO Inference API with client URL (cURL) 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, cURL inference, and Python inference. All of these methods produce the same JSON response format.
|
||||
|
||||
!!! Example "Detect Model JSON Response"
|
||||
|
||||
=== "`ultralytics`"
|
||||
|
||||
```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())
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```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"
|
||||
|
||||
```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, cURL inference, and Python inference. All of these methods produce the same JSON response format.
|
||||
|
||||
!!! Example "Segment Model JSON Response"
|
||||
|
||||
=== "`ultralytics`"
|
||||
|
||||
```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())
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```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"
|
||||
|
||||
```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, cURL inference, and Python inference. All of these methods produce the same JSON response format.
|
||||
|
||||
!!! Example "Pose Model JSON Response"
|
||||
|
||||
=== "`ultralytics`"
|
||||
|
||||
```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())
|
||||
```
|
||||
|
||||
=== "cURL"
|
||||
|
||||
```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"
|
||||
|
||||
```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 loading="lazy" 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`, `int8` |
|
||||
| [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`, `int8` |
|
||||
| [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`, `half`, `int8` |
|
||||
| [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!
|
||||
130
docs/en/hub/models.md
Normal file
130
docs/en/hub/models.md
Normal file
@ -0,0 +1,130 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn how to efficiently train AI models using Ultralytics HUB, a streamlined solution for model creation, training, evaluation, and deployment.
|
||||
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 custom datasets.
|
||||
|
||||
The process is user-friendly and efficient, involving a simple three-step creation and accelerated training powered by Ultralytics YOLOv8. Real-time updates on model metrics are available during training, allowing users to monitor progress at each step. Once training is completed, models can be previewed and easily deployed to real-world applications. Therefore, Ultralytics HUB offers a comprehensive yet straightforward system for model creation, training, evaluation, and deployment.
|
||||
|
||||
The entire process of training a model is detailed on our [Cloud Training Page](cloud-training.md).
|
||||
|
||||
|
||||
|
||||
## Train Model
|
||||
|
||||
Navigate to the [Models](https://hub.ultralytics.com/models) page by clicking on the **Models** button in the sidebar.
|
||||
|
||||
Training a model using HUB is a 4-step process:
|
||||
|
||||
- **Execute the pre-requisites script**: Run the provided scripts to prepare the virtual environment.
|
||||
- **Provide the API and start Training**: Once the model is prepared, provide the API key as instructed and execute the code block.
|
||||
- **Check the results and Metrics**: Upon successful execution, a link is provided to the Metrics Page. This page offers comprehensive details on the trained model, including specifications, loss metrics, dataset information, and image distributions. Additionally, the 'Deploy' tab provides access to the trained model's documentation and license details.
|
||||
- **Test your model**: Ultralytics HUB offers testing using custom images, device cameras, or links to test on `iPhone` or `Android` devices.
|
||||
|
||||
|
||||
|
||||
!!! 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 to trigger the **Train Model** dialog.
|
||||
|
||||
The **Train Model** dialog has three simple steps:
|
||||
|
||||
### 1. Dataset
|
||||
|
||||
Select the dataset for training and click **Continue**.
|
||||
|
||||
|
||||
|
||||
### 2. Model
|
||||
|
||||
Choose the project, model name, and architecture. Read more about available architectures in our [YOLOv8](https://docs.ultralytics.com/models/yolov8) (and [YOLOv5](https://docs.ultralytics.com/models/yolov5)) documentation.
|
||||
|
||||
Click **Continue** when satisfied with the configuration.
|
||||
|
||||
|
||||
|
||||
!!! 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.
|
||||
|
||||
Advanced options are available to modify this behavior.
|
||||
|
||||
## Preview Model
|
||||
|
||||
Ultralytics HUB offers various ways to preview trained models.
|
||||
|
||||
You can upload an image in the **Test** card under the **Preview** tab to preview your model.
|
||||
|
||||
|
||||
|
||||
Use our Ultralytics Cloud API to effortlessly [run inference](inference-api.md) with your custom model.
|
||||
|
||||
|
||||
|
||||
Preview your model in real-time 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) device by [downloading](https://ultralytics.com/app_install) our [Ultralytics HUB Mobile Application](app/index.md).
|
||||
|
||||
|
||||
|
||||
## Train the model
|
||||
|
||||
Ultralytics HUB offers three training options:
|
||||
|
||||
- **Ultralytics Cloud** - Learn more about training via the Ultralytics [Cloud Training Page](cloud-training.md)
|
||||
- **Google Colab**
|
||||
- **Bring your own agent**
|
||||
|
||||
## Training the Model on Google Colab
|
||||
|
||||
To start training using Google Colab, follow the instructions 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>
|
||||
|
||||
|
||||
|
||||
## Bring your own Agent
|
||||
|
||||
Create an API endpoint through Ultralytics HUB to train the Model locally. Follow the provided steps, and access training details via the link generated on the Agent terminal.
|
||||
|
||||
|
||||
|
||||
## Deploy Model
|
||||
|
||||
Export your model to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle, and more.
|
||||
|
||||
|
||||
|
||||
## Share Model
|
||||
|
||||
Ultralytics HUB's sharing functionality provides a convenient way to share models. Control the general access of your models, setting them to "Private" or "Unlisted".
|
||||
|
||||
Navigate to the Model page, open the model actions dropdown, and click on the **Share** option.
|
||||
|
||||
|
||||
|
||||
Set the general access and click **Save**.
|
||||
|
||||
|
||||
|
||||
Now, anyone with the direct link can view your model.
|
||||
|
||||
!!! tip "Tip"
|
||||
|
||||
Easily copy the model's link shown in the **Share Model** dialog by clicking on it.
|
||||
|
||||
|
||||
|
||||
## Edit and Delete Model
|
||||
|
||||
Navigate to the Model page, open the model actions dropdown, and click on the **Edit** option to update the model. To delete the model, select the **Delete** option.
|
||||
|
||||
|
||||
34
docs/en/hub/on-premise/index.md
Normal file
34
docs/en/hub/on-premise/index.md
Normal file
@ -0,0 +1,34 @@
|
||||
---
|
||||
description: Discover what's next for Ultralytics with our under-construction page, previewing new, groundbreaking AI and ML features coming soon.
|
||||
keywords: Ultralytics, coming soon, under construction, new features, AI updates, ML advancements, YOLO, technology preview
|
||||
---
|
||||
|
||||
# Under Construction 🏗️🌟
|
||||
|
||||
Welcome to the Ultralytics "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you!
|
||||
|
||||
## Exciting New Features on the Way 🎉
|
||||
|
||||
- **Innovative Breakthroughs:** Get ready for advanced features and services that will transform your AI and ML experience.
|
||||
- **New Horizons:** Anticipate novel products that redefine AI and ML capabilities.
|
||||
- **Enhanced Services:** We're upgrading our services for greater efficiency and user-friendliness.
|
||||
|
||||
## Stay Updated 🚧
|
||||
|
||||
This placeholder page is your first stop for upcoming developments. Keep an eye out for:
|
||||
|
||||
- **Newsletter:** Subscribe [here](https://ultralytics.com/#newsletter) for the latest news.
|
||||
- **Social Media:** Follow us [here](https://www.linkedin.com/company/ultralytics) for updates and teasers.
|
||||
- **Blog:** Visit our [blog](https://ultralytics.com/blog) for detailed insights.
|
||||
|
||||
## We Value Your Input 🗣️
|
||||
|
||||
Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/contact).
|
||||
|
||||
## Thank You, Community! 🌍
|
||||
|
||||
Your [contributions](https://docs.ultralytics.com/help/contributing) inspire our continuous [innovation](https://github.com/ultralytics/ultralytics). Stay tuned for the big reveal of what's next in AI and ML at Ultralytics!
|
||||
|
||||
---
|
||||
|
||||
Excited for what's coming? Bookmark this page and get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖
|
||||
180
docs/en/hub/projects.md
Normal file
180
docs/en/hub/projects.md
Normal file
@ -0,0 +1,180 @@
|
||||
---
|
||||
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.
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Gc6K5eKrTNQ"
|
||||
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 YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
|
||||
</p>
|
||||
|
||||
## 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**.
|
||||
|
||||
|
||||
58
docs/en/hub/quickstart.md
Normal file
58
docs/en/hub/quickstart.md
Normal file
@ -0,0 +1,58 @@
|
||||
---
|
||||
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
|
||||
|
||||
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 loading="lazy" 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>
|
||||
|
||||
## Creating an Account
|
||||
|
||||
[Ultralytics HUB](https://hub.ultralytics.com/) offers multiple easy account creation options. Users can register and sign in using Google, Apple, GitHub accounts, or a work email address.
|
||||
|
||||
|
||||
|
||||
## The Dashboard
|
||||
|
||||
Upon logging in, users are directed to the HUB dashboard, providing a comprehensive overview. The left pane conveniently offers links for tasks such as Uploading Datasets, Creating Projects, Training Models, Integrating Third-party Applications, Accessing Support, and Managing Trash.
|
||||
|
||||
|
||||
|
||||
## Selecting the Model
|
||||
|
||||
Choose a Dataset and train the model by selecting the Project name, Model name, and Architecture. Ultralytics offers a range of YOLOv8, YOLOv5, and YOLOv5u6 Architectures, including pre-trained and custom options.
|
||||
|
||||
Read more about Models on the [HUB Models page](models.md).
|
||||
|
||||
## Training the Model
|
||||
|
||||
There are three ways to train your model: using Google Colab, training locally, or through Ultralytics Cloud. Learn more about training options on the [Cloud Training Page](cloud-training.md).
|
||||
|
||||
## Integrating the Model
|
||||
|
||||
Integrate your trained model with third-party applications or connect HUB from an external agent. Ultralytics HUB currently supports simple one-click API Integration with Roboflow. Read more about integration on the [Integration Page](integrations.md).
|
||||
|
||||
## Need Help?
|
||||
|
||||
If you encounter any issues or have questions, we're here to assist you. You can report a bug, request a feature, or ask a question.
|
||||
|
||||
|
||||
|
||||
## Data Management
|
||||
|
||||
Manage your datasets efficiently with options to restore or permanently delete them from the Trash section in the left column.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user