Object detection has a close relationship with analysing videos and images, which is why it has gained a lot of attention to so many researchers in recent years. In this article, I will introduce you to a machine learning project on object detection with Python.
What is Object Detection?
To gain a full understanding of the image, we should not only focus on classifying the different images but also try to accurately estimate the concepts and locations of the objects contained in each image. This task is known as object detection. Detecting Objects usually consists of different subtasks such as face detection, pedestrian detection, Number plate detection and skeleton detection.
Also, Read – 100+ Machine Learning Projects Solved and Explained.
As one of the fundamental problems of computer vision, object detection is able to provide valuable information for the semantic understanding of images and videos and is related to many applications, including the classification of images, analysis of human behaviour, facial recognition and autonomous driving.
In the meantime, inheriting from neural networks and other machine learning systems, advancements in these areas will allow the development of neural networks and will also have great impacts on the techniques of detecting objects that can be considered as the future machine learning systems.
Object Detection with Python
In this section, I will take you through a Machine Learning project on Object Detection with Python. Here I use the Yolo V5 model for detecting cars in an image or by using a camera. Let’s start by importing the necessary Python libraries for this task:
import os, time, random import numpy as np import pandas as pd import cv2, torch from tqdm.auto import tqdm import shutil as sh from IPython.display import Image, clear_output import matplotlib.pyplot as plt
Now before using the Yolo V5 model you need to install some dependencies for that you can just use these two commands below in your terminal or command prompt:
git clone https://github.com/ultralytics/yolov5 # clone repo pip install -qr yolov5/requirements.txt # install dependencies
Now let’s load and prepare the dataset for the task of objects detection with Python:
Now let’s have a look at the sample image from the dataset before moving further:
Now let’s train the machine learning model for detecting objects with Python:
Detecting Objects
We have successfully trained our model, now we need to test the model on the images to see if our model is working well to detect objects in the images:
predicted_files = []
for (dirpath, dirnames, filenames) in os.walk("inference/output"):
predicted_files.extend(filenames)
Image(filename=f'inference/output/{random.choice(predicted_files)}')
I hope you liked this article on machine learning project on Object Detection with Python. Feel free to ask your valuable questions in the comments section below.
