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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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10
docs/en/hub/app/android.md
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@ -4,7 +4,7 @@ description: Experience real-time object detection on Android with Ultralytics.
keywords: Ultralytics, Android app, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, hardware delegates, mobile AI, download app
---
# Ultralytics Android App: Real-time Object Detection with YOLO Models
# Ultralytics Android App: Real-time [Object Detection](https://www.ultralytics.com/glossary/object-detection) with YOLO Models
<a href="https://ultralytics.com/hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-android-app-detection.avif" alt="Ultralytics HUB preview image"></a>
@ -29,7 +29,7 @@ keywords: Ultralytics, Android app, real-time object detection, YOLO models, Ten
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>&nbsp;
</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.
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](https://www.ultralytics.com/glossary/tensorflow) Lite for model optimization and various hardware delegates for acceleration, enabling fast and efficient object detection.
<p align="center">
<br>
@ -44,7 +44,7 @@ The Ultralytics Android App is a powerful tool that allows you to run YOLO model
## 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.
To achieve real-time performance on your Android device, YOLO models are quantized to either FP16 or INT8 [precision](https://www.ultralytics.com/glossary/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](https://www.ultralytics.com/glossary/accuracy).
### FP16 Quantization
@ -52,7 +52,7 @@ FP16 (or half-precision) quantization converts the model's 32-bit floating-point
### 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.
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](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) due to the lower numerical precision.
!!! tip "mAP Reduction in INT8 Models"
@ -65,7 +65,7 @@ Different delegates are available on Android devices to accelerate model inferen
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).
4. **NNAPI**: The Android [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) 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:

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docs/en/hub/app/index.md
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@ -31,11 +31,11 @@ keywords: Ultralytics HUB, YOLO models, mobile app, iOS, Android, hardware accel
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>&nbsp;
</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.
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](https://www.ultralytics.com/glossary/neural-network-nn) 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.
- **Run YOLOv5 and YOLOv8 models**: Experience the power of YOLO models on your mobile device for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and [image recognition](https://www.ultralytics.com/glossary/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.

6
docs/en/hub/app/ios.md
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@ -4,7 +4,7 @@ description: Discover the Ultralytics iOS App for running YOLO models on your iP
keywords: Ultralytics, iOS App, YOLO models, real-time object detection, Apple Neural Engine, Core ML, FP16 quantization, INT8 quantization, machine learning
---
# Ultralytics iOS App: Real-time Object Detection with YOLO Models
# Ultralytics iOS App: Real-time [Object Detection](https://www.ultralytics.com/glossary/object-detection) with YOLO Models
<a href="https://ultralytics.com/hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-android-app-detection.avif" alt="Ultralytics HUB preview image"></a>
@ -44,7 +44,7 @@ The Ultralytics iOS App is a powerful tool that allows you to run YOLO models di
## 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.
To achieve real-time performance on your iOS device, YOLO models are quantized to either FP16 or INT8 [precision](https://www.ultralytics.com/glossary/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](https://www.ultralytics.com/glossary/accuracy).
### FP16 Quantization
@ -56,7 +56,7 @@ INT8 (or 8-bit integer) quantization further reduces the model's size and comput
## 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.
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](https://www.ultralytics.com/glossary/machine-learning-ml) tasks, particularly for [neural networks](https://www.ultralytics.com/glossary/neural-network-nn), 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.

2
docs/en/hub/cloud-training.md
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@ -44,7 +44,7 @@ Most of the times, you will use the Epochs training. The number of epochs can be
!!! note
When using the Epochs training, the [account balance](./pro.md#account-balance) is deducted after every epoch.
When using the Epochs training, the [account balance](./pro.md#account-balance) is deducted after every [epoch](https://www.ultralytics.com/glossary/epoch).
Also, after every epoch, we check if you have enough [account balance](./pro.md#account-balance) for the next epoch. In case you don't have enough [account balance](./pro.md#account-balance) for the next epoch, we will stop the training session, allowing you to resume training your model from the last checkpoint saved.

4
docs/en/hub/index.md
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@ -49,7 +49,7 @@ We hope that the resources here will help you get the most out of HUB. Please br
## Introduction
[Ultralytics HUB](https://www.ultralytics.com/hub) is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It also offers a range of pre-trained models to choose from, making it extremely easy for users to get started. Once a model is trained, it can be effortlessly previewed in the [Ultralytics HUB App](app/index.md) before being deployed for real-time classification, object detection, and instance segmentation tasks.
[Ultralytics HUB](https://www.ultralytics.com/hub) is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It also offers a range of pre-trained models to choose from, making it extremely easy for users to get started. Once a model is trained, it can be effortlessly previewed in the [Ultralytics HUB App](app/index.md) before being deployed for real-time classification, [object detection](https://www.ultralytics.com/glossary/object-detection), and [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) tasks.
<p align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"
@ -117,7 +117,7 @@ Ultralytics HUB allows you to manage and organize your projects efficiently. You
### What integrations are available with Ultralytics HUB?
Ultralytics HUB offers seamless integrations with various platforms to enhance your machine learning workflows. Some key integrations include:
Ultralytics HUB offers seamless integrations with various platforms to enhance your [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) workflows. Some key integrations include:
- **Roboflow:** For dataset management and model training. Learn more on the [Integrations](integrations.md) page.
- **Google Colab:** Efficiently train models using Google Colab's cloud-based environment. Detailed steps are available in the [Google Colab](https://docs.ultralytics.com/integrations/google-colab/) section.

12
docs/en/hub/inference-api.md
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@ -112,12 +112,12 @@ curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
See the table below for a full list of available inference arguments.
| Argument | Default | Type | Description |
| -------- | ------- | ------- | -------------------------------------------------------------------- |
| `file` | | `file` | Image or video file to be used for inference. |
| `imgsz` | `640` | `int` | Size of the input image, valid range is `32` - `1280` pixels. |
| `conf` | `0.25` | `float` | Confidence threshold for predictions, valid range `0.01` - `1.0`. |
| `iou` | `0.45` | `float` | Intersection over Union (IoU) threshold, valid range `0.0` - `0.95`. |
| Argument | Default | Type | Description |
| -------- | ------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `file` | | `file` | Image or video file to be used for inference. |
| `imgsz` | `640` | `int` | Size of the input image, valid range is `32` - `1280` pixels. |
| `conf` | `0.25` | `float` | Confidence threshold for predictions, valid range `0.01` - `1.0`. |
| `iou` | `0.45` | `float` | [Intersection over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU) threshold, valid range `0.0` - `0.95`. |
## Response

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docs/en/hub/integrations.md
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@ -96,7 +96,7 @@ Navigate to the [Integrations](https://hub.ultralytics.com/settings?tab=integrat
### Exports
After you [train a model](./models.md#train-model), you can [export it](./models.md#deploy-model) to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle and many others.
After you [train a model](./models.md#train-model), you can [export it](./models.md#deploy-model) to 13 different formats, including ONNX, OpenVINO, CoreML, [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), Paddle and many others.
![Ultralytics HUB screenshot of the Deploy tab inside the Model page with an arrow pointing to the Export card and all formats exported](https://github.com/ultralytics/docs/releases/download/0/ultralytics-hub-deploy-export-formats.avif)

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docs/en/hub/models.md
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@ -219,7 +219,7 @@ Furthermore, you can preview your model in real-time directly on your [iOS](http
## Deploy Model
After you [train a model](#train-model), you can export it to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle and many others.
After you [train a model](#train-model), you can export it to 13 different formats, including ONNX, OpenVINO, CoreML, [TensorFlow](https://www.ultralytics.com/glossary/tensorflow), Paddle and many others.
![Ultralytics HUB screenshot of the Deploy tab inside the Model page with an arrow pointing to the Export card and all formats exported](https://github.com/ultralytics/docs/releases/download/0/ultralytics-hub-deploy-export-formats.avif)

2
docs/en/hub/quickstart.md
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@ -6,7 +6,7 @@ keywords: Ultralytics HUB, Quickstart, YOLO models, dataset upload, project mana
# Ultralytics HUB Quickstart
[Ultralytics HUB](https://www.ultralytics.com/hub) is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It also offers a range of pre-trained models to choose from, making it extremely easy for users to get started. Once a model is trained, it can be effortlessly previewed in the [Ultralytics HUB App](app/index.md) before being deployed for real-time classification, object detection, and instance segmentation tasks.
[Ultralytics HUB](https://www.ultralytics.com/hub) is designed to be user-friendly and intuitive, allowing users to quickly upload their datasets and train new YOLO models. It also offers a range of pre-trained models to choose from, making it extremely easy for users to get started. Once a model is trained, it can be effortlessly previewed in the [Ultralytics HUB App](app/index.md) before being deployed for real-time classification, [object detection](https://www.ultralytics.com/glossary/object-detection), and [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) tasks.
<p align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/lveF9iCMIzc?si=_Q4WB5kMB5qNe7q6"