Object Detection Python in YOLOv8: Guided Exploration

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Introduction

Object detection is a critical task in computer vision, enabling machines to identify and locate objects within images or videos. YOLO (You Only Look Once) is a popular family of real-time object detection algorithms known for its speed and accuracy. 

YOLOv8, the latest iteration in the YOLO series, builds upon its predecessors to provide even better performance. In this article, we will explore how to perform object detection in Python using YOLOv8. 

What is YOLOv8?

YOLOv8, released in October 2023, represents the latest iteration in the You Only Look Once (YOLO) family of object detection algorithms. Known for their balance between speed and accuracy, YOLO models are popular for real-time applications like self-driving cars and video surveillance.

What is Y OLOv8?

YOLOv8, short for You Only Look Once version 8, is an open-source object detection framework that belongs to the YOLO family. It is developed by Alexey Bochkovskiy and is based on the YOLOv4 architecture. YOLOv8 introduces various improvements, including enhanced speed, accuracy, and ease of use.

Building upon its predecessors, YOLOv8 boasts several key improvements:

  • Enhanced Speed: YOLOv8 achieves significant speed gains, pushing real-time inference on a Tesla V100 GPU to over 2000 FPS for small object detection. This makes it ideal for computationally constrained environments.
  • Accuracy Boost: YOLOv8 maintains YOLO’s reputation for accuracy, even with the speed improvements. On the COCO benchmark dataset, it achieves an impressive mAP (mean Average Precision) of 60.3%, surpassing previous YOLO models and even some more complex detection algorithms.
  • Modular Design: YOLOv8 features a modular design, allowing users to adjust specific components like the neck and head architecture for customization and fine-tuning to specific tasks. This flexibility expands its potential applications. 

Overall, YOLOv8 offers a compelling combination of speed, accuracy, and modularity, making it a strong contender for various real-time object detection tasks. Whether you prioritize speed, accuracy, or both, YOLOv8 deserves consideration for your next project What is New in YOLOv8. 

Getting Started How to Detect Object Python in YOLOv8:

To begin with object detection using YOLOv8 in Python, follow these steps:

1: Install Dependencies: 

Ensure you have the necessary dependencies installed. You can install them using the following commands:

  • bash
  • pip install torch
  • pip install torchvision
  • pip install opencv-python

2: Clone YOLOv8 Repository:

Clone the YOLOv8 repository from GitHub using the following command:

  • bash
  • git clone https://github.com/ultralytics/yolov5.git

Navigate to the YOLOv5 directory:

  • bash
  • cd yolov5

3: Download Pre-trained Weights:

Download the YOLOv5 pre-trained weights using:

  • bash
  • bash weights/download_weights.sh

This will download the pre-trained weights for various YOLOv5 models.

Object Detection with YOLOv8:

Now that you have set up the environment, you can perform object detection on images or videos using YOLOv8. Create a Python script and use the following code as a starting point:

  • python
  • import torch
  • from pathlib import Path
  • from models. Experimental import attempt load
  • from utils. general import check_img_size, non_max_suppression, scale_coords
  • from tosspots import plot_one_box
  • from utils. torch_utils import select_device, time synchronized

# Load YOLOv8 Model

  • weights = ‘yolov5s.pt’ # Replace with the path to the desired pre-trained weights
  • device = select_device (”) # Use default device (GPU if available, else CPU)
  • model = attempt_load (weights, map_location=device) # Load the YOLOv8 model

# Set image size

  • img_size = check_img_size (640, s=model.stride.max ())

# Load image

  • img_path = ‘path/to/your/image.jpg’ # Replace with the path to your image
  • img0 = cv2.imread(img_path) # OpenCV reads image in BGR format

# Inference

  • img = letterbox (img0, new_shape=img_size) [0]
  • img = img [, -1]. transpose (2, 0, 1) # BGR to RGB, to 3xHxW
  • img = np. ascontiguousarray (img)
  • img = torch. from NumPy(img).to(device)
  • img = img. float ()  # uint8 to fp16/32
  • img /= 255.0 # 0 – 255 to 0.0 – 1.0
  • if img. Ndimension () == 3:
  • img = img. unsqueeze (0)

# Inference

  • pred = model (img, augment=False) [0]

# NMS (Non-Maximum Suppression)

  • pred = non_max_suppression (pred, 0.4, 0.5)

# Process detections

  • for i, det in enumerate(pred):  # detections for image i
  • s = ”
  • for *xyxy, conf, cls in reversed(det):
  • label = f'{names[int(cls)]} {conf:.2f}’
  • plot_one_box (xyxy, img0, label=label, color=colors[int(cls)], line_thickness=3)

# Display result

  • cv2.imshow(‘YOLOv8 Object Detection’, img0)
  • cv2.waitKey(0)
  • cv2.destroyAllWindows()

This script loads a pre-trained YOLOv8 model, performs inference on an input image, applies non-maximum suppression, and displays the results.

Conclusion

YOLOv8 is a powerful tool for real-time object detection, and integrating it into your Python projects is relatively straightforward. By following the steps outlined in this article, you can leverage the capabilities of YOLOv8 to identify and locate objects within images or videos with ease. 

Experiment with different configurations, and fine-tune the model for your specific use case to achieve optimal performance.

FAQS (Frequently Asked Questions)

Q#1: What is YOLOv8, and how does it differ from previous versions?

YOLOv8, or You Only Look Once version 8, is a real-time object detection algorithm that belongs to the YOLO family. It differs from previous versions by introducing improvements in terms of accuracy and speed. YOLOv8 employs a more advanced architecture, often referred to as YOLOv4-CSP, which stands for YOLO version 4 with Cross-Stage Partial networks. This architecture enhances performance while maintaining real-time capabilities for object detection tasks.  

Q#2: How can I install YOLOv8 in my Python environment?

You can install YOLOv8 by using the official GitHub repository maintained by Ultralytics. The process typically involves cloning the repository, installing the required dependencies, and downloading the pre-trained weights. The official YOLOv8 repository provides detailed instructions for installation on different operating systems. Commonly used tools like PyTorch and CUDA are prerequisites for running YOLOv8 efficiently.

Q#3: What are the key steps to perform object detection with YOLOv8 in Python?

Object detection using YOLOv8 involves several steps. Firstly, you need to prepare your dataset and annotations. Then, configure the YOLOv8 model according to your requirements. Training the model on your dataset comes next, and finally, you can perform inference on new images or videos. The Ultralytics repository provides example scripts and documentation to guide you through each step. Fine-tuning on a specific dataset or using pre-trained weights can also be beneficial for achieving better performance.

Q#4: Can YOLOv8 detect custom objects, and how can I train it on my own dataset?

Yes, YOLOv8 can be trained to detect custom objects. To train YOLOv8 on your own dataset, you need to create annotations for your images in the YOLO format. These annotations specify the bounding boxes and class labels for each object. After preparing the dataset, you can use the YOLOv8 training script, specifying the custom dataset path, number of classes, and other relevant parameters. Training will update the model to recognize the specified custom objects.

Q#5: What hardware requirements are recommended for running YOLOv8 in real-time?

YOLOv8, especially when running in real-time, benefits from powerful GPUs. A GPU with CUDA support is highly recommended to accelerate the inference process. The specific requirements depend on the desired performance and the size of the model. YOLOv8 is compatible with a variety of GPUs, and the Ultralytics repository provides guidance on selecting the appropriate settings for different hardware configurations. Additionally, running YOLOv8 on a machine with sufficient RAM and processing power contributes to optimal performance during training and inference. 

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