Image classification services

Our expertise

Our expertise in building purpose-built image classification algorithms

We are your one-stop provider for the best image classification algorithms — and we help businesses of all types and sizes extract meaning and value from their image libraries.

01

Binary classification

Track every step of the production process, ensuring that your products meet high-quality standards. You can use binary classification to detect product defects and reduce the amount of defective products. This will save you time and money and help maintain your company's reputation.

02

Multi-class classification

Build multi-class image classification algorithms on Python with OpenCV.ai to go beyond simple yes or no. It helps classify pictures from a wider range of predefined classes. Equipped with multi-class image classification, you can identify plant and crop types, animal species, clothing types, brands, or colors and improve your efficiency.

03

Multi-label classification

Such multi-label image classification models can apply multiple relevant labels to a single image. Use it to help your cameras identify suspicious activity and objects, or speed up listings and targeted marketing with automated tagging capabilities.

04

Hierarchical classification

Embed a hierarchical image classification model in your processes to simplify complex image datasets, improve search and retrieval, and gain deeper insights. The algorithms will create a well-organized class structure with detailed subcategories and find specific images faster and easier.

05

Unsupervised classification

Your image library holds a huge potential, and unsupervised deep learning image classification can help you uncover it. It is a great asset to analyze vast collections of photos. Unsupervised classification enables manufacturers to identify anomalies in products, or healthcare workers to uncover patterns in disease and accelerate medical discovery.

06

Supervised classification

If you are struggling to categorize your ever-growing collection of images, supervised 3D image classification is your weapon. We train custom AI models to recognize and classify your images exactly as you need them, enabling healthcare companies to make faster diagnoses, manufacturing companies to improve quality control, e-commerce companies to effortlessly categorize products, and more.

07

Image classification-based CV solutions

Unlock the power of your visual data with image classification computer vision solutions. Our team’s profound expertise in the domain allows us to address even the most complex challenges and build fit-for-purpose CV solutions to our clients’ benefit. Image classification models, object tracking and detection algorithms, deep learning, and CNN technologies – we will take advantage of all the possible knowledge and tech stack we possess to make sure you get the most out of your budget.

Ready to elevate your business US?

OpenCV.ai provides image classification services to boost your business efficiency.

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OPENCV.AI in numbers

Discover our expertise in numbers

With years of experience building custom software solutions for startups and enterprises alike, OpenCV.ai specializes in custom computer vision software development. Take a look at some numbers that represent our achievements as a computer vision software provider.

CONSULTING process

Key steps of image classification service with OpenCV.ai

Image classification software development services use the latest advanced technologies and approaches to help you build your image classifier. At OpenCV.ai, we use best practices to create a superior prototype that brings your ideas to life through collaboration & rapid execution.

01

Strategic discovery

Conduct an in-depth consultation to understand your business needs and challenges
Gather key insights and business objectives
Learn how image classification algorithms can be used to achieve your goals

02

Project planning

Develop a comprehensive project roadmap
Define the project’s scope, timelines, resource allocation, and critical milestones

03

Data preparation

Create a database of images extracted from multiple sources
Ensure that the data is structured, relevant, and of high quality
Train the model using passive or active learning image classification techniques

04

Technical implementation

Design and build the selected image classifier
Maintain open lines of communication with you for adjustments to the project as it evolves

05

Solution deployment

Launch the solution or integrate it into your existing systems
Facilitate a seamless launch to increase your solution’s value from the outset

05

Maintenance

Ensure the solution’s smooth implementation and continued success
Provide expert guidance and ongoing support

Our case studies

Success stories.

Hundreds completed projects and counting.

Explore our extensive portfolio and unlock the secrets of visual intelligence with our collection of success stories. See how AI image classification and other computer vision services have transformed businesses, contributed to technological advancements, and improved user experiences.

Portfolio

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Management

The team at your service.

The most talented AI software developers, QA engineers, and data scientists who enjoy solving challenging tasks with progressive technologies. We ensure you get qualified expert AI-managed services with half of our team represented by senior developers.

CEO

ANNA
KOGAN

CTO

Tatiana
Khanova

CDO

Grigory
Serebryakov

Chief Scientist and President

Dr. Gary
Bradski

Expectations

Unlock excellence with OpenCV.ai’s computer vision image classification

Navigating the complexities of image classification in computer vision projects can seem daunting, but with advanced computer vision services, organizations can get expert guidance and support at every stage. Our team of consultants and engineers provides AI image classification services to ensure the success of your visual AI initiatives.

Clear & efficient Roadmap

We don't just jump in.

Our well-defined roadmap ensures clarity, quality, and speed, aligning expectations and explaining the workflow and associated investments to our clients. In addition, OpenCV.ai prioritizes transparency in communication so you will always know what to expect and where your project stands.

Visual data expertise

Our team is not just technically skilled – they are visual data enthusiasts.

We have a deep understanding of visual subtleties and the ability to translate your business needs into effective AI image classification models. This experience of working with complex visual data allows us to bring a unique skill set to tackle even challenging tasks.

Data security

We employ rigorous security protocols to ensure the safety and integrity of your data throughout its lifecycle, from storage to processing to training.

This includes robust encryption, secure data transmission, and access control to protect your valuable data from unauthorized access and cyber threats.

Proven quality

Our quality control reflects best practices in project management, relying on well-structured planning, execution, and delivery.

With a proven track record of delivering high-quality image classification solutions, we also emphasize regular monitoring and maintenance to keep your systems running at peak performance.

Leading technology.

Build an image classifier with the OpenCV.ai team and take advantage of the latest advances in machine learning and artificial intelligence.

We stay at the forefront of the most innovative tools and techniques to build only the most robust and accurate models and deliver more value to you.

High customization

We understand that a one-size-fits-all approach doesn't work for image classification algorithms in deep learning projects.

Every business has unique requirements, which is why OpenCV.ai offers a high degree of customization to tailor the solution to your specific data, needs, and industry.

Tech stack we use

Technology we utilize to classify images

As a leading provider of computer vision services, OpenCV.ai leverages leading and traditional image classification algorithms and expertise to extract valuable insights from visual information.

Modern image classification service combines state-of-the-art models, deep learning models, and image processing techniques to enable businesses to uncover patterns and gain a deeper understanding of their visual data.

Tools

Pytorch

TensorFlow

OpenCV

CUDA/cuDNN

Core ML

TensorRT

CVAT

JAX

ONNX

OpenVINO

Deepstream SDK

TFLite

Hardware

ARM

RISC V

Jetson

NVIDIA GPU

Raspberry Pi

Intel / AMD CPU

Intel Myriad X

Questions?
Answers.

Are you limited to the OpenCV library?

Despite being linked to the popular OpenCV library, our team at OpenCV.ai doesn't limit our work to just this library. We provide AI image classification services to assist our clients in enhancing their business performance through visual intelligence.

What is the best AI model for image classification?

There's no single best AI model for image classification, as the optimal choice depends on several factors. These include task specificity (what objects you are classifying), computational resources and their requirements, and a focus on either speed (real-time solutions) or accuracy. The best AI model will be one that is custom-built to meet your needs and business specifics.

What is the use of image classification?

Image classification models enable machines to recognize and interpret any objects depicted in images. The technology finds its usage in various applications such as facial recognition, medical image analysis, autonomous vehicles, retail for recognizing products, and surveillance for security purposes. Image classification can also assist in sorting and organizing large collections of photos, making them searchable and accessible.

What is the best AI for images?

There's no single best AI model for image classification, as the optimal choice depends on several factors. These include task specificity (what objects you are classifying), computational resources and their requirements, and a focus on either speed (real-time solutions) or accuracy. The best AI model will be one that is custom-built to meet your needs and business specifics.

What is an example of image classification in AI?

For example, an AI model can be trained to distinguish between images of cats, dogs, and other animals. Once trained, the model can take an unlabeled image as input and output a classification (a cat or a dog) based on the visual features it has learned. This technology can be applied to a wide range of subjects, from cars to flowers to medical X-rays, and is widely used in the surveillance, manufacturing, and e-commerce industries.

How to do image classification?

The image classification process involves several key steps. First, you need to collect and preprocess a diverse set of images to ensure that they are properly labeled for training. Next, you select an appropriate model architecture, such as a CNN, and train it on your dataset. After training, it's important to evaluate the model's performance using a separate validation set. Finally, once you're satisfied with the model's accuracy, you can use it to classify new, unseen images.

Which algorithm is best for image classification?

When it comes to image classification, Convolutional Neural Networks are frequently cited as the best algorithm. CNNs are specifically designed to handle image data using convolutional layers to extract features and patterns within images. Their hierarchical structure allows them to learn complex representations, which makes them highly effective for tasks that involve visual recognition.

What are the 5 steps involved in image classification?

Image classification typically consists of the following five stages:

Data Collection: Gathering a comprehensive dataset of images relevant to the classification task.
Data Preprocessing: Preparing the images for analysis (resizing, labeling, etc.).
Model Selection and Training: Choosing an appropriate model and training it on the preprocessed data.
Model Evaluation: Assessing the model's performance to ensure it generalizes well to new data.
Launch: Integrating the trained model into a system where it can classify new images.

Is computer vision part of ML or DL?

Computer vision is a multidisciplinary field that encompasses both ML and DL. Traditional ML techniques can be applied to simpler computer vision tasks, such as edge detection or simple object recognition. However, DL can learn from large amounts of data and capture complex patterns. As a result, it has become the cornerstone of modern computer vision solutions, especially for more challenging tasks such as image classification and object recognition.

What is the CNN algorithm in computer vision?

The CNN algorithm is the foundation of computer vision solutions. It is a type of deep neural network that is particularly well-suited for analyzing visual data. CNNs use convolutional layers that can automatically and efficiently extract important features from images, such as edges, textures, and shapes. This makes CNN a powerful tool for tasks such as image classification, where it can learn to distinguish between different objects or categories with high accuracy.

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