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ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)

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Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
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2023-05-09 21:20:34 +02:00
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docs/hub/app/android.md
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---
comments: true
description: Run YOLO models on your Android device for real-time object detection with Ultralytics Android App. Utilizes TensorFlow Lite and hardware delegates.
---
# Ultralytics Android App: Real-time Object Detection with YOLO Models
@ -19,7 +20,7 @@ FP16 (or half-precision) quantization converts the model's 32-bit floating-point
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
@ -61,4 +62,4 @@ To get started with the Ultralytics Android App, follow these steps:
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.
With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.

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docs/hub/app/index.md
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---
comments: true
description: Experience the power of YOLOv5 and YOLOv8 models with Ultralytics HUB app. Download from Google Play and App Store now.
---
# Ultralytics HUB App

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---
comments: true
description: Get started with the Ultralytics iOS app and run YOLO models in real-time for object detection on your iPhone or iPad with the Apple Neural Engine.
---
# Ultralytics iOS App: Real-time Object Detection with YOLO Models
@ -33,7 +34,6 @@ By combining quantized YOLO models with the Apple Neural Engine, the Ultralytics
| 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
@ -52,4 +52,4 @@ To get started with the Ultralytics iOS App, follow these steps:
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.
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.

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docs/hub/datasets.md
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---
comments: true
description: Upload custom datasets to Ultralytics HUB for YOLOv5 and YOLOv8 models. Follow YAML structure, zip and upload. Scan & train new models.
---
# HUB Datasets
@ -46,4 +47,4 @@ names:
After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab.
Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 or YOLOv8 models on it!
<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/216763338-9a8812c8-a4e5-4362-8102-40dad7818396.png">
<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/216763338-9a8812c8-a4e5-4362-8102-40dad7818396.png">

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docs/hub/index.md
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---
comments: true
description: 'Ultralytics HUB: Train & deploy YOLO models from one spot! Use drag-and-drop interface with templates & pre-training models. Check quickstart, datasets, and more.'
---
# Ultralytics HUB
@ -20,7 +21,6 @@ comments: true
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

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docs/hub/inference_api.md
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This page is currently under construction!️ 👷Please check back later for updates. 😃🔜
# 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.
@ -45,7 +44,6 @@ print(response.json())
In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `path/to/image.jpg` with the path to the image you want to analyze.
## Example Usage with CLI
You can use the YOLO Inference API with the command-line interface (CLI) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
@ -334,7 +332,6 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
}
```
### Pose Model Format
YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.

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---
comments: true
description: Train and Deploy your Model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle or directly on Mobile.
---
# HUB Models
@ -11,7 +12,6 @@ Connect to the Ultralytics HUB notebook and use your model API key to begin trai
<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>
## Deploy to Real World
Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run