| # 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 |