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---
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
```
#yolo11 |