Yolov5n V7.0

🚀 YOLOv5 Object Detection

This project provides an implementation of YOLOv5 for object detection tasks in computer vision, leveraging the power of the YOLO architecture to detect objects in images.

🚀 Quick Start

To quickly get started with this project, you can follow these steps:

📦 Installation

Install the yolov5 library using the following command:

pip install -U yolov5

💻 Usage Examples

Basic Usage

Load the model and perform prediction:

import yolov5

# load model
model = yolov5.load('fcakyon/yolov5n-v7.0')
  
# 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)

# inference with larger input size
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 --img 640 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 10 --device cuda:0

📄 License

This project is licensed under the GPL-3.0 license.