Ultralytics HUB Docs

docs/app/android.md

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+# Ultralytics HUB Android Application: Real-time Object Detection with YOLO Models
+
+The Ultralytics HUB Android Application is a powerful tool that allows you to run YOLO models directly on your Android device for real-time object detection. The app utilizes TensorFlow Lite for model optimization and various hardware delegates for acceleration, enabling fast and efficient object detection.
+
+## 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
+
+To get started with the Ultralytics HUB Android Application, follow these steps:
+
+1. Download the Ultralytics HUB Android Application 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://bit.ly/ultralytics_hub).
+
+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 HUB Android Application, 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.

docs/app/index.md

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+# Ultralytics HUB Mobile Application
+
+<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" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a>
+  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
+  <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
+    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></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="" /></a>&nbsp;
+  <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>
+</div>
+
+Welcome to the Ultralytics HUB Mobile Application!
+
+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 Ultralytics HUB Mobile Application, 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.
+
+## Key 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 Ultralytics HUB Mobile Application.
+- **Mobile Compatibility**: The Ultralytics HUB Mobile Application supports both iOS and Android devices, bringing the power of YOLO models to a wide range of users.
+
+## 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 Mobile Application on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go.
+
+Don't forget to check out our comprehensive [Ultralytics HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.

docs/app/ios.md

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+# Ultralytics HUB iOS Application: Real-time Object Detection with YOLO Models
+
+The Ultralytics HUB iOS Application is a powerful tool that allows you to run YOLO models directly on your iPhone or iPad for real-time object detection. The app utilizes the Apple Neural Engine and Core ML for model optimization and acceleration, enabling fast and efficient object detection.
+
+## 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
+
+To get started with the Ultralytics HUB iOS Application, follow these steps:
+
+1. Download the Ultralytics HUB iOS Application 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://bit.ly/ultralytics_hub).
+
+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 HUB iOS Application, 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.

docs/css/main.css

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+/* Table format like GitHub ----------------------------------------------------------------------------------------- */