Top 17 Image Recognition Machine Learning Projects (Updated for 2024)

Image recognition is the task of identifying objects of interest in an image and identifying to which class the image belongs. Although various human vision simulation methods have been developed, a common goal of image recognition machine learning projects is classifying recognized objects into different classes, otherwise known as object detection.

The adoption of image recognition applications has been on the rise and has accelerated even further due to COVID-19. The global image recognition market size is projected to reach USD 86.32 billion by 2027, exhibiting a growth of 17.6% during the forecast period according to the latest report by Fortune business insights. With 60% projected growth in North America alone and a 116% increase in job demand for computer vision engineers, practicing image recognition machine learning projects is important for any aspiring data scientist.

Here are 17 different image recognition machine learning project ideas ranging from beginner to expert level.

Face recognition is a computer vision technology that is a subdomain of object recognition that tries to observe instances of semantic objects. Detection is a simpler problem to solve than recognition, so it makes a great beginner project.

The complexity of accurate human facial recognition arises from factors such as too many faces in images, odd expressions, illuminations, low resolution, face occlusion, skin color, and distance from and orientation to the computer.

How You Can Do It: In this project, you will learn how to implement real-time face recognition in OpenCV using Python and the Haar Cascade Classifier algorithm.

This is a beginner-friendly project, and you can follow this tutorial from TechVidvan for more details on how to practice it.

BONUS: If you liked this project and want to try your hand at facial recognition, check out this tutorial too.

Image classification is a deep learning project that applies supervised and unsupervised machine learning techniques.

Supervised classification: Select samples for each target class and train a neural network with these target class samples, then classify the new samples.

Unsupervised classification: Group sample images into clusters of images with similar features. Each cluster is then classified into the specified class.

How You Can Do It: CIFAR-10 is a computer vision dataset composed of a collection of 10 images of different classes, such as cars, birds, dogs, horses, ships, and trucks.

In this project, you will create a classification model that can identify which class an image belongs to. Follow this tutorial from Dataflair for more detailed steps and get started with the official CIFAR dataset.

This project uses Keras and TensorFlow to build, train, and test a Convolutional Neural Network (CNN) capable of identifying the breed of a dog in a supplied image. This is a supervised learning problem, specifically a multiclass classification problem.

How You Can Do It: You will learn how to use a dog breed dataset to create a classification model that can identify different dog breeds from a single image.

The project is part of Kaggle’s Playground Prediction Competition, and you can find more information on the competition rules and dataset as well as some code and instructions on their website.

The use of image recognition methods in medical diagnosis is a growing trend. A 2020 study by IDtechX compared the image recognition performance of more than 60 companies in medical diagnostics, which is estimated to be worth more than $3 billion by 2030 as it has applications in cancer, respiratory, and retinal segments.

How You Can Do It: You can use this dataset of breast cancer histopathology images to build an image classification model to determine whether the patient has cancer-based on cell characteristics.

For implementation guidance, check out this tutorial by Abhinav Sagar, explaining the steps of building and analyzing a CNN. The code for the project can be found in his GitHub repository.

Color recognition can be used for visual tasks such as creating green screen applications, using simple photo editing software, organizing LEGO bricks, and identifying countries in passport photos. This application will help you practice using OpenCV, NumPy, and pandas Python packages.

How You Can Do It: Here are some machine learning image datasets that can be used for the project:

• Google-512 dataset
• LEGO colors
• Passport colors

You can follow DataFlair’s tutorial to learn more about implementing such a project.

Object tracking in the video is a slightly advanced computer vision task that focuses on estimating the state of a target object present in a scene with respect to its previous location. It is now widely used in equipment inspection, military surveillance, animal modeling, and many applications.

An object tracking model performs two tasks: predicting the next state of the object and correcting that state with respect to the actual state of the object. Object tracking models find applications in traffic control and human-computer interaction.

How You Can Do It: Here are some video datasets that you can use for this computer vision task:

• TLP Datasets
• Tracking Net
• MOT17

Follow this tutorial by Adrian Rosebrock for simple object tracking with OpenCV for more detailed steps. The project will help you learn how to implement centroid tracking, a simple and easy-to-understand yet highly effective tracking algorithm, before using more advanced kernel-based and correlation-based tracking algorithms.

Combine what you’ve learned in object tracking and image classification projects to create a model that detects and recognizes LEGO bricks in real-time using a webcam or phone camera. This project enables you to experiment with different types of machine vision, classification, and decision-making programs to deploy to embedded systems.

How You Can Do It: Here are the datasets for this project:

• LEGO vs. generic brick identification set
• Images of LEGO bricks

Get started with this tutorial by Digikey using the OpenMV H7 camera module, which trains a machine learning model to identify a particular piece with Edge Impulse and then deploy that model to the OpenMV.

Similar image detection has many uses, especially in businesses such as e-commerce and product recommendation engines. Google’s image search uses a similar technique as both looks for similar images related to the product and list all websites that contain those images. A McKinsey & Company report attributed 35% of Amazon’s sales to recommendations.

How You Can Do It: In this project, Anson Wont uses a technique called transfer learning when training VGG. The hard work is done by simply reusing the trained weights to build a new model. The details of this process can be found in his tutorial.

Another approach is to use a dataset like Imagenette, as the pre-trained models found online are usually trained on the ImageNet dataset and can extract meaningful feature vectors from these images. You can follow Sanjaya Subedi’s tutorial on using deep neural networks for more details.

Wearing a mask has become mandatory since the spread of the COVID-19 virus. Unfortunately, some people still do not follow this policy. Therefore, it has become necessary to create a system that automatically recognizes someone not wearing a mask.

Data scientists can use Python to build a two-stage mask detector by taking a dataset of facemask images and training a mask classifier using Keras/TensorFlow then importing a facial recognition program to identify faces with and without masks.

How You Can Do It: This system is difficult to implement, but you can use the guidance from this Adrian Rosebrock tutorial to learn how to do it. Additionally, Prajna Bhandary has uploaded a source code of the project to her GitHub repository.

With fires in the Amazonian rainforest and recent events in California, an early fire detection system is desperately needed. This is still a difficult problem to solve, but some data scientists are using deep learning and OpenCV to build custom InceptionV3 and CNN architectures for indoor and outdoor fire detection.

How You Can Do It: You can check out this tutorial by Dhruvil Shah in which you will learn how to model a customized basic CNN architecture inspired by AlexNet architecture then examine the model’s limitations and create a customized InceptionV3 model.

Law enforcement agencies tasked with fighting crime and detecting offending vehicles are increasingly relying on computer vision solutions that can read a vehicle’s license plate and help them punish violators. ِAccording to the latest report by the US Department of Justice, law enforcement agencies using automated license plate recognition systems reported increases in stolen vehicle recoveries (68%), arrests (55%), and productivity (50%).

How You Can Do It: In this project, you will use Pytesseract, Imutils, and the OpenCV Python library to create license plate recognition programs.

The source code for building the project is here, and you can follow this tutorial by Simon Kiruri for instructions.

Multinational corporations such as Google, Mercedes, and Tesla are developing self-driving or driverless cars. One of the algorithms that researchers for these companies run to ensure 100% road safety and driving accuracy is traffic sign recognition.

How You Can Do It: In this project, you will build a model that uses CNN and Keras to classify traffic signs in an image into multiple classes.

This project requires some prior knowledge of deep learning libraries, Python Language, and CNN. You can use a dataset of 50,000 traffic sign images on Kaggle and follow this tutorial by Shikha Gupta to learn how to create such a model.

This project is based on one of the most popular and readily available pattern recognition datasets, the iris classification dataset, which contains 3 classes related to a species of iris, each with 50 instances.

How You Can Do It: This beginner project can help you gain hands-on experience with image classification while training a model that can predict new iris types.

You can follow this tutorial by Injemamul Irshad, which walks you through the steps of building a model and testing different algorithms to find the best fit.

The intricacies of pistachio classification are rooted in factors like their various sizes, shades of color, possible defects, and the lighting conditions under which photos are taken. This dataset is perfect for beginners to intermediate learners, given its reliance on neural networks.

How You Can Do It: In this project, you can use TensorFlow or PyTorch in combination with Python. You will process the dataset, create and train your model, and evaluate its performance. To have a comprehensive overview of how to manipulate and read this dataset, you can also follow this notebook as a guide.

This fun family photo face detection project gathers original raw data from your family album and creates a facial recognition model to identify family members in photos.

In this project, to create a complete face recognition system, you must work on 3 distinct phases:

• Face detection and data gathering
• Recognizer training
• Facial recognition

How You Can Do It: Label your data with free annotation tools and train a model in less than a few hours. This project is a multi-step process including face detection, face alignment, feature extraction, and feature detection. You can follow this tutorial by Marcelo Rovai as a guide.

Like license plate readers, lane recognition is another computer vision model that has played a key role in the development of self-driving cars.

How You Can Do It: This beginner-friendly project will help you learn more about image and video classification. It is a Python OpenCV lane detection project consisting of 6 algorithms:

• Capturing and decoding video file frame by frame
• Converting the image to grayscale
• Applying filters to reduce noise in video frames
• Detecting the edge using the canny edge method
• Finding the region of interest and focusing on that part
• Detecting lanes using Hough Line Transform

For more guidance, follow the steps in this tutorial by Angel Jude Suarez which contains the datasets and source code.

The intricacy of precise fashion product recognition emerges from challenges such as varying product designs, intricate patterns, overlapping items, various fabrics, differences in fashion seasons, and diverse camera angles and lighting conditions.

How You Can Do It:

In this project, you will get hands-on experience in building a fashion product recognition system using TensorFlow and Convolutional Neural Network (CNN).

This project is of intermediate complexity, ideal for individuals who have a foundational understanding of computer vision and are looking to delve deeper into its applications. You can also follow Marlesson’s notebook as a guide.

Navigating the complexities of landscape recognition presents its own challenges, from diverse geographical features to varying weather conditions and lighting.

How You Can Do It:

Utilize TensorFlow and Convolutional Neural Networks (CNN). This project is ideal for those with basic machine learning knowledge, and offers a chance to explore computer vision techniques on 12,000 diverse landscape images.

It’s a perfect stepping stone to enhance your skills in image classification, offering hands-on experience with a wide-ranging dataset.

Recognizing kitchen utensils is a fun project, given their different shapes and materials. It’s also a great way to get hands-on experience with image classification and deepen your understanding of machine learning in a practical setting.

How You Can Do It:

In this project, you’ll first explore the dataset to see what kinds of utensil images you have. Then, you’ll prepare these images by making sure they all fit the same size and look similar in terms of lighting and color, so your computer can understand them better.

Using TensorFlow, a tool for building machine learning models, you’ll create a Convolutional Neural Network (CNN). As it learns, you’ll keep tweaking it until it gets really good at knowing what utensil it’s looking at, even if it’s a picture it hasn’t seen before.

If you want more projects to further develop your skills, try out our new Takehomes in which you solve longer problems step-by-step using notebooks from different companies.

Takehomes will help you build your data science skills, including Python, SQL, and machine learning, and allow you to test projects used by high-profile companies.

Additionally, you can look at other data science project lists and datasets from Interview Query:

• Top 12 Classification Machine Learning Projects and Datasets
• 21 Computer Vision Machine Learning Interview Questions
• 41 Free Datasets for Your Next Data Science Project
• Top 30 Data Science Projects with Source Code