Number and Operator Detection Based on YOLO11x
This repository contains a PyTorch-exported model for detecting box using the YOLO11 architecture. The model has been trained to recognize these symbols in images and return their locations and classifications.
Model Description
The YOLO11 model is optimized for detecting the following:
- helmet / face(no helmet)
#class
helmet
face (no helmet)
How to Use
To use this model in your project, follow the steps below:
1. Installation
Ensure you have the ultralytics library installed, which is used for YOLO models:
pip install ultralytics
2. Load the Model
You can load the model and perform detection on an image as follows:
from ultralytics import YOLO
# Load the model
model = YOLO("./helmet-11x.pt")
# Perform detection on an image
results = model("image.png")
# Display or process the results
results.show() # This will display the image with detected objects
3. Model Inference
The results object contains bounding boxes, labels (e.g., numbers or operators), and confidence scores for each detected object.
Access them like this:
for result in results:
print(result.boxes) # Bounding boxes
print(result.names) # Detected classes
print(result.scores) # Confidence scores
(yolo11x)
mAP50 0.958612
mAP50-95 0.649457
precision 0.913453
recall 0.930291
#yolo11