metadata

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:

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("./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:

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

#yolo11