xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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

Browse Source 
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-05-09 21:20:34 +02:00
committed by GitHub
parent 6ee3a9a74b
commit d1107ca4cb
138 changed files with 744 additions and 351 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
docs/hub/app/android.md
View File

@ -1,5 +1,6 @@
---
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.

1
docs/hub/app/index.md
View File

@ -1,5 +1,6 @@
---
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

4
docs/hub/app/ios.md
View File

@ -1,5 +1,6 @@
---
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.