YOLOv8s_test Open-Source Object Detection Model - Lightweight Architecture Empowers Real-Time Detection Tasks

Yolov8s Test

Developed by iarbel

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 #Pre-trained models

Downloads 24

Release Time : 1/3/2024

Model Overview

YOLOv8s is the small version in the YOLOv8 series, designed for efficient object detection and supports detection of 80 common object categories.

Model Features

Efficient detection

Capable of efficiently detecting multiple objects in real-time scenarios, suitable for embedded devices and edge computing.

Multi-class support

Supports detection of 80 common object categories, covering various scenarios in daily life.

Easy to use

Provides simple APIs and pre-trained models for quick integration and deployment.

Model Capabilities

Object detection

Real-time inference

Multi-class recognition

Use Cases

Security surveillance

Pedestrian detection

Used for pedestrian detection in surveillance scenarios to enhance the intelligence of security systems.

Autonomous driving

Traffic object recognition

Identifies vehicles, pedestrians, traffic signs, etc., on the road to assist decision-making in autonomous driving systems.

Retail analytics

Product recognition

Identifies products on shelves for inventory management and customer behavior analysis.

🚀 ultralyticsplus/yolov8s

This project is based on Ultralytics and uses YOLOv8 for object detection tasks, providing high - precision vision solutions.

🚀 Quick Start

This section will guide you through the basic steps of using the ultralyticsplus/yolov8s model for object detection.

✨ Features

Object Detection: Capable of detecting a wide range of objects, including but not limited to people, vehicles, animals, and daily items.
High Precision: Achieves a mAP (mean Average Precision) of 0.449.

📦 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

The model supports the following labels for object detection:

['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']

Model Information

Property	Details
Model Type	ultralyticsplus/yolov8s
Training Data	Not provided
Metrics	mAP: 0.449

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