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--- |
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language: |
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- en |
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library_name: ultralytics |
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pipeline_tag: object-detection |
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tags: |
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- yolo |
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- object-detect |
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- yolo11 |
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- yolov11 |
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--- |
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# Rock Paper Scissors Detection Based on YOLO11x |
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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. |
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## Model Description |
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The YOLO11x model is optimized for detecting the following: |
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- **Rock** |
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- **Paper** |
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- **Scissors** |
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## How to Use |
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To use this model in your project, follow the steps below: |
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### 1. Installation |
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Ensure you have the `ultralytics` library installed, which is used for YOLO models: |
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```bash |
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pip install ultralytics |
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``` |
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### 2. Load the Model |
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You can load the model and perform detection on an image as follows: |
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```python |
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from ultralytics import YOLO |
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# Load the model |
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model = YOLO("./rps_11x.pt") |
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# Perform detection on an image |
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results = model("image.png") |
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# Display or process the results |
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results.show() # This will display the image with detected objects |
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``` |
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### 3. Model Inference |
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The results object contains bounding boxes, labels (e.g., numbers or operators), and confidence scores for each detected object. |
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Access them like this: |
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```python |
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for result in results: |
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print(result.boxes) # Bounding boxes |
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print(result.names) # Detected classes |
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print(result.scores) # Confidence scores |
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``` |
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#yolo11 |
