---
language:
- en
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo
- object-detect
- yolo11
- yolov11
---

# Number and Operator Detection Based on YOLO11x

This repository contains a PyTorch-exported model for detecting sidewalk, car, sign and so on.. using the YOLO11s architecture. The model has been trained to recognize these symbols in images and return their locations and classifications.

## Model Description

The YOLO11s model is optimized for detecting the following:

```text
#class
Braille_block
car
downstairs
driveway
sidewalk
sign_green
sign_red
upstairs
```
## 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:

```bash
pip install ultralytics
```

### 2. Load the Model

You can load the model and perform detection on an image as follows:
```python
from ultralytics import YOLO

# Load the model
model = YOLO("./yolo11m-sidewalk.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:

```python
for result in results:
    print(result.boxes)   # Bounding boxes
    print(result.names)   # Detected classes
    print(result.scores)  # Confidence scores
```

![](result.png)

#yolo11