Complete Ultralytics Tutorial: Object Detection with YOLO

Ultralytics is a Python framework that provides state-of-the-art implementations for YOLO (You Only Look Once), one of the most popular and efficient object detection algorithms. In this tutorial, we will learn how to use Ultralytics from installation to custom model deployment.

What is Ultralytics and YOLO?

YOLO is a real-time object detection algorithm that can detect multiple objects in a single forward pass of a neural network. Ultralytics provides implementations of YOLOv8, YOLOv9, YOLOv10, and YOLO11 that are easy to use with high performance.

• Very simple and intuitive API
• Support for multiple tasks: detection, segmentation, classification, pose estimation
• Pretrained models for various use cases
• Easy training and finetuning
• Export to various formats (ONNX, TensorRT, CoreML, etc.)

Installation

1. Setup Environment

# Create virtual environment
python -m venv ultralyticsenv

source ultralyticsenv/bin/activate  # Linux/Mac
ultralyticsenv\Scripts\activate  # Windows


Install Ultralytics
pip install ultralytics

For GPU support (CUDA)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

import ultralytics
from ultralytics import YOLO

Check version
print(f"Ultralytics version: {ultralytics.version}")

Check CUDA availability
import torch
print(f"CUDA available: {torch.cuda.isavailable()}")
if torch.cuda.isavailable():

    print(f"GPU: {torch.cuda.getdevicename(0)}")

from ultralytics import YOLO

Load YOLOv8 model (available: yolov8n, yolov8s, yolov8m, yolov8l, yolov8x)
n=nano, s=small, m=medium, l=large, x=extra large
model = YOLO('yolov8n.pt')  # Model is automatically downloaded

Or load YOLO11 (latest)
model = YOLO('yolo11n.pt')

from ultralytics import YOLO

Load model
model = YOLO('yolov8n.pt')

Inference on single image
results = model('path/to/image.jpg')

Inference on multiple images
results = model(['image1.jpg', 'image2.jpg', 'image3.jpg'])

Inference from URL
results = model('https://example.com/image.jpg')

Process results
for result in results:
    # Bounding boxes
    boxes = result.boxes
    print(f"Detected {len(boxes)} objects")

    for box in boxes:
        # Box coordinates (x1, y1, x2, y2)
        xyxy = box.xyxy[0].tolist()

        # Confidence score
        confidence = box.conf[0].item()

        # Class ID and name
        classid = int(box.cls[0].item())
        classname = model.names[classid]

        print(f"  {classname}: {confidence:.2f} at {xyxy}")

from ultralytics import YOLO
import cv2

model = YOLO('yolov8n.pt')

Inference on video file
results = model('path/to/video.mp4', stream=True)

for result in results:
    # Process each frame
    annotatedframe = result.plot()

    # Display (optional)
    cv2.imshow('Detection', annotatedframe)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

from ultralytics import YOLO
import cv2

model = YOLO('yolov8n.pt')

Open webcam
cap = cv2.VideoCapture(0)

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    # Run inference
    results = model(frame, verbose=False)

    # Visualize results