README.md

metadata

tags:
  - Tyre Quality
  - Tyre Classification
  - Image Classification
  - Machine Learning
  - Pytorch
  - Deep Learning
  - Computer Vision
  - Prediction
  - yolov8
  - yolo
library_name: ultralytics
library_version: 8.0.43
language:
  - en
pipeline_tag: image-classification
model-index:
  - name: foduucom/Tyre-Quality-Classification
    results:
      - task:
          type: image-classification
        metrics:
          - type: accuracy
            value: 0.835
            name: Top1_acc

#Welcome to the repository of our state-of-the-art image classification model, uniquely fine-tuned on the robust architecture of YOLOv8s, tailored to distinguish between defective and good tires with unprecedented accuracy.

Model Details

Model Description

Our model leverages the cutting-edge capabilities of YOLOv8s, renowned for its speed and precision in object detection, which has been meticulously fine-tuned for the specific domain of tire quality assessment. This model emerges as an indispensable tool for automating quality control in tire manufacturing, ensuring that every tire meets the highest standards of safety and performance.

• Developed by: FODUU AI
• Model type: Image Classification
• Task: Classifies tyres with a high degree of accuracy

Supported Labels

['Good_Tyre','Defective_Tyre']

Key Features:

• High Precision Classification: Classifies tires with a high degree of accuracy, reducing the margin of error significantly compared to traditional methods.
• Rapid Assessment: Optimized for quick image processing, allowing for real-time quality control on the production line.
• Robust Training: Trained on a diverse dataset of tire images, capturing a wide range of defects to ensure reliability in various operational environments.
• Easy Integration: Designed to seamlessly fit into existing manufacturing systems, facilitating a smooth transition from manual to automated quality assessment.

How to Use This Model

This model is hosted on Hugging Face, making it incredibly easy to integrate and deploy. You can directly use our pre-trained model for classifying your tire images by following the instructions in our usage documentation.

Potential Applications

• Manufacturing Quality Control: Streamline the process of identifying defective tires, ensuring that only the best-quality products reach the market.
• Safety Compliance: Assist in meeting rigorous safety standards by detecting flaws that could compromise tire integrity.
• Automotive Service Centers: Provide quick and reliable tire checks, enhancing customer service and safety.
• Research and Development: Aid in the analysis of tire wear and degradation, contributing to the development of longer-lasting tire materials.

Getting Started

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43

from ultralyticsplus import YOLO, render_result
import cv2

# load model
model = YOLO('foduucom/Tyre-Quality-Classification')

# 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 = '/path/to/your/document/images'

# perform inference
results = model.predict(image)

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