Rock Paper Scissors

🚀 Rock Paper Scissors Object Detection Model

This YOLO v10 small model, created by FRC Team 578, is designed for educational purposes to illustrate how an object detection model works.

🚀 Quick Start

To use this model, you need to install the necessary libraries and download the model weights. Here are the steps:

pip install ultralytics
pip install huggingface_hub

✨ Features

• Educational Purpose: This model is trained for educational purposes, helping students understand how an object detection model works.
• YOLO v10: It is based on the YOLO v10 architecture, which is known for its efficiency in object detection.

📦 Installation

You can install the required libraries using pip:

pip install ultralytics
pip install huggingface_hub

💻 Usage Examples

Basic Usage

from ultralytics import YOLO
from huggingface_hub import hf_hub_download
from matplotlib import pyplot as plt

# Load the weights from our repository
model_path = hf_hub_download(
    local_dir=".",
    repo_id="fairportrobotics/rock-paper-scissors",
    filename="model.pt"
)
model = YOLO(model_path)

# Load a test image
sample_path = hf_hub_download(
    local_dir=".",
    repo_id="fairportrobotics/rock-paper-scissors",
    filename="sample.jpg"
)

# Do the predictions
res = model.predict(
    source=sample_path,
    project='.',
    name='detected',
    exist_ok=True,
    save=True,
    show=False,
    show_labels=True,
    show_conf=True,
    conf=0.5
)

plt.figure(figsize=(15,10))
plt.imshow(plt.imread('detected/sample.jpg'))
plt.show()

Advanced Usage

Use the model with your webcam:

from ultralytics import YOLO
import cv2
import math 
from huggingface_hub import hf_hub_download

# start the webcam
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)

# Load the weights from our repository
model_path = hf_hub_download(
    local_dir=".",
    repo_id="fairportrobotics/rock-paper-scissors",
    filename="model.pt"
)
model = YOLO(model_path)

# object classes
classNames = ["rock", "paper", "scissors"]

while True:
    success, img = cap.read()
    results = model(img, stream=True)

    # coordinates
    for r in results:
        boxes = r.boxes

        for box in boxes:
            # bounding box
            x1, y1, x2, y2 = box.xyxy[0]
            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # convert to int values

            # put box in cam
            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)

            # confidence
            confidence = math.ceil((box.conf[0]*100))/100

            # class name
            cls = int(box.cls[0])

            # object details
            org = [x1, y1]
            font = cv2.FONT_HERSHEY_SIMPLEX
            fontScale = 1
            color = (255, 0, 0)
            thickness = 2

            cv2.putText(img, classNames[cls] + " " + str(round(confidence,2)), org, font, fontScale, color, thickness)

    cv2.imshow('Webcam', img)
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

📚 Documentation

Training Data

The model was trained on 100 images found online, and no image augmentation was performed.

Metrics

📄 License

This project is licensed under the GPL license.