YOLOv8s Open-Source Object Detection Model - Lightweight Architecture Empowers Real-Time Object Detection Tasks

Yolov8s

Developed by ultralyticsplus

YOLOv8s is a lightweight object detection model based on Ultralytics' YOLOv8 architecture, suitable for real-time object detection tasks.

Object Detection

PyTorch

#Multi-class Object Detection #Real-time Visual Analysis #YOLOv8 Architecture

Downloads 708

Release Time : 1/12/2023

Model Overview

YOLOv8s is an efficient object detection model capable of identifying and localizing various common objects in images, ideal for real-time applications.

Model Features

Efficient Object Detection

Capable of efficiently detecting various common objects in real-time scenarios.

Lightweight Model

The model has a small size, making it suitable for deployment on resource-limited devices.

Multi-class Support

Supports detection of 80 different object categories, covering everyday common objects.

Model Capabilities

Object Detection

Real-time Inference

Multi-class Recognition

Use Cases

Security Surveillance

Real-time Monitoring

Used for real-time detection of people, vehicles, and other objects in surveillance cameras.

Can identify and label target objects in surveillance footage in real-time.

Autonomous Driving

Road Object Detection

Detects vehicles, pedestrians, traffic signs, etc., on the road.

Helps autonomous driving systems recognize the surrounding environment.

Smart Retail

Product Recognition

Identifies product categories and their locations on shelves.

Assists in inventory management and customer behavior analysis.

🚀 ultralyticsplus/yolov8s

This project is based on the Ultralytics framework, using the YOLOv8s model for object detection tasks, offering high - precision object detection capabilities.

🚀 Quick Start

This model is used for object detection. You can follow the steps below to quickly start using this model.

✨ Features

Object Detection: Capable of detecting a wide range of objects, including people, vehicles, animals, and daily items.
High Precision: Achieves a mAP of 0.449 in object detection tasks.

📦 Installation

Install ultralyticsplus:

pip install -U ultralyticsplus==0.0.14

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('ultralyticsplus/yolov8s')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()

📚 Documentation

Supported Labels

['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']

📄 License

This project is licensed under the AGPL - 3.0 license.

📄 Model Information

Property	Details
Model Type	ultralyticsplus/yolov8s
Library Name	ultralytics
Library Version	8.0.4
Task Type	object - detection
mAP	0.449

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