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

Use `conda install -c conda-forge ultralytics` (#4272)

Browse Source 
Co-authored-by: Yash Khurana <ykhurana6@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: MLBoy_DaisukeMajima <rockyshikoku@gmail.com>
Co-authored-by: Louis Lac <lac.louis5@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
Co-authored-by: Simon Lévesque <simlevesque@dismail.de>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
single_channel
Glenn Jocher 1 year ago committed by GitHub
parent 22474e9ad5
commit 48748ded24
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 25 additions and 21 deletions
Show Stats Download Patch File Download Diff File

2
docs/help/contributing.md
Unescape View File

@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, open-source, contribute, pull request, bug report,
# Contributing to Ultralytics Open-Source YOLO Repositories
First of all, thank you for your interest in contributing to Ultralytics open-source YOLO repositories! Your contributions will help improve the project and benefit the community. This document provides guidelines and best practices for contributing to Ultralytics YOLO repositories.
First of all, thank you for your interest in contributing to Ultralytics open-source YOLO repositories! Your contributions will help improve the project and benefit the community. This document provides guidelines and best practices to get you started.
## Table of Contents

42
docs/modes/predict.md
Unescape View File

@ -54,21 +54,22 @@ YOLOv8 can process different types of input sources for inference, as shown in t
Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.
| Source | Argument | Type | Notes |
|-------------|--------------------------------------------|---------------------------------------|----------------------------------------------------------------------------|
| image | `'image.jpg'` | `str` or `Path` | Single image file. |
| URL | `'https://ultralytics.com/images/bus.jpg'` | `str` | URL to an image. |
| screenshot | `'screen'` | `str` | Capture a screenshot. |
| PIL | `Image.open('im.jpg')` | `PIL.Image` | HWC format with RGB channels. |
| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` of `uint8 (0-255)` | HWC format with BGR channels. |
| numpy | `np.zeros((640,1280,3))` | `np.ndarray` of `uint8 (0-255)` | HWC format with BGR channels. |
| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` of `float32 (0.0-1.0)` | BCHW format with RGB channels. |
| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
| video ✅ | `'video.mp4'` | `str` or `Path` | Video file in formats like MP4, AVI, etc. |
| directory ✅ | `'path/'` | `str` or `Path` | Path to a directory containing images or videos. |
| glob ✅ | `'path/*.jpg'` | `str` | Glob pattern to match multiple files. Use the `*` character as a wildcard. |
| YouTube ✅ | `'https://youtu.be/Zgi9g1ksQHc'` | `str` | URL to a YouTube video. |
| stream ✅ | `'rtsp://example.com/media.mp4'` | `str` | URL for streaming protocols such as RTSP, RTMP, or an IP address. |
| Source | Argument | Type | Notes |
|---------------|--------------------------------------------|-----------------|---------------------------------------------------------------------------------------------|
| image | `'image.jpg'` | `str` or `Path` | Single image file. |
| URL | `'https://ultralytics.com/images/bus.jpg'` | `str` | URL to an image. |
| screenshot | `'screen'` | `str` | Capture a screenshot. |
| PIL | `Image.open('im.jpg')` | `PIL.Image` | HWC format with RGB channels. |
| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
| numpy | `np.zeros((640,1280,3))` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` | BCHW format with RGB channels `float32 (0.0-1.0)`. |
| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
| video ✅ | `'video.mp4'` | `str` or `Path` | Video file in formats like MP4, AVI, etc. |
| directory ✅ | `'path/'` | `str` or `Path` | Path to a directory containing images or videos. |
| glob ✅ | `'path/*.jpg'` | `str` | Glob pattern to match multiple files. Use the `*` character as a wildcard. |
| YouTube ✅ | `'https://youtu.be/Zgi9g1ksQHc'` | `str` | URL to a YouTube video. |
| stream ✅ | `'rtsp://example.com/media.mp4'` | `str` | URL for streaming protocols such as RTSP, RTMP, or an IP address. |
| multi-stream ✅ | `'list.streams'` | `str` or `Path` | `*.streams` text file with one stream URL per row, i.e. 8 streams will run at batch-size 8. |
Below are code examples for using each source type:
@ -262,16 +263,19 @@ Below are code examples for using each source type:
results = model(source, stream=True) # generator of Results objects
```
=== "Stream"
Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols.
=== "Streams"
Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols. If mutliple streams are provided in a `*.streams` text file then batched inference will run, i.e. 8 streams will run at batch-size 8, otherwise single streams will run at batch-size 1.
```python
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Define source as RTSP, RTMP or IP streaming address
source = 'rtsp://example.com/media.mp4'
# Single stream with batch-size 1 inference
source = 'rtsp://example.com/media.mp4' # RTSP, RTMP or IP streaming address
# Multiple streams with batched inference (i.e. batch-size 8 for 8 streams)
source = 'path/to/list.streams' # *.streams text file with one streaming address per row
# Run inference on the source
results = model(source, stream=True) # generator of Results objects

2
docs/quickstart.md
Unescape View File

@ -28,7 +28,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
```bash
# Install the ultralytics package using conda
conda install ultralytics
conda install -c conda-forge ultralytics
```
=== "Git clone"

Write Preview
Loading…
Cancel
Save