Yolo V5 Rock Paper Scissors Detection

🚀 YOLOv5 Object Detection Model

This project is based on YOLOv5, a powerful object detection model. It provides high - precision object detection capabilities and can be easily integrated into various vision - related projects.

✨ Features

• High Precision: Achieves a mAP@0.5 of 0.966445241976421.
• Easy to Use: Simple installation and usage process.
• Customizable: Can be fine - tuned on custom datasets.

📦 Installation

Install yolov5:

pip install -U yolov5

💻 Usage Examples

Basic Usage

Load model and perform prediction:

import yolov5

# load model
model = yolov5.load('uisikdag/taskagitmakas')
  
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')

Advanced Usage

Finetune the model on your custom dataset:

yolov5 train --data data.yaml --img 640 --batch 16 --weights uisikdag/taskagitmakas --epochs 10

📚 Documentation

The dataset can be found at Link.

More models available at: awesome - yolov5 - models

📄 Model Information