-YOLOv8-Object-Detection-Toolkit

Training Neural Network YoloV8 for detection

🚀 YOLOv8 Object Detection Toolkit Welcome to the YOLOv8 Object Detection Toolkit - Your One-Stop Solution for Object Detection Tasks!

Overview This repository provides a comprehensive toolkit for using YOLOv8 (You Only Look Once version 8) for object detection. With YOLOv8, you can efficiently detect and locate objects within images and video streams.

Prerequisites Ensure that you have the following dependencies installed:

📦 Ultralytics: Install it with pip install ultralytics. Getting Started

1. Training Your Model Train your YOLOv8 model using your dataset:

Unzip your data folder:

shell Copy code !unzip '/content/drive/MyDrive/final_data.v1i.yolov8(1).zip' -d test1 Load a model using a YAML configuration:

python Copy code from ultralytics import YOLO model = YOLO("yolov8n.yaml") # Load a pretrained model (recommended for training) Train your model using your dataset:

python Copy code model.train(data="/content/test1/data.yaml", epochs=400, patience=0) Evaluate model performance on the validation set:

python Copy code metrics = model.val() # Evaluate model performance on the validation set 2. Resuming Training In case of an interruption, you can resume training:

python Copy code model.train(resume=True) 3. Object Detection Perform object detection with your trained model or a pre-trained one:

Load a model:

python Copy code from ultralytics import YOLO model = YOLO('/content/yolov8.pt') Detect objects in an image:

python Copy code pred = model.predict('/content/test1/final_data.v1i.yolov8/test/images/Image__2022-12-04__14-19-42-i-_bmp.rf.e8ca4b7d4fc8c856f036a9e0d28127ca.jpg') 4. Single Image Detection Perform object detection on a single image and display the result with bounding boxes:

python Copy code import time model = YOLO('/content/V8(n)(3).pt')

image_path = '/content/frame__000007.jpg' results = model.predict(image_path)

result = results[0]

from PIL import Image Image.fromarray(result.plot()[:, :, ::-1]) 5. Test and Save Images Test and save images with bounding boxes in a designated folder:

python Copy code import matplotlib.pyplot as plt import matplotlib.image as mpimg

folder_path = "/content/output"

results = model.predict('/content/frame__000002.jpg')

result = results[0]

from PIL import Image image = Image.fromarray(result.plot()[:, :, ::-1)

Save the image in the specified folder.

file_name = "image.png" full_file_path = os.path.join(folder_path, file_name) image.save(full_file_path)

Author Aryan Singh Dalal