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

# Rock Paper Scissors Detection Based on YOLO11x

This repository contains a PyTorch-exported model for detecting R.P.S. using the YOLO11x architecture. The model has been trained to recognize these symbols in images and return their locations and classifications.

## Model Description

The YOLO11x model is optimized for detecting the following:

- **Rock**
- **Paper**
- **Scissors**
## 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("./rps_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:

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

![](result.png)

#yolo11