Training Data for Computer Vision

A vast amount of computer vision training data is required for developing AI models that can detect, identify, classify, and track various objects. We train data to enable machines to think, see, observe, and understand.

We also automate monitoring & surveillance applications to sense, analyze, and interpret digital images, videos, and other visual inputs for making appropriate recommendations or actions.

Contact Us Now
Computer Vision Training Data

Computer Vision Datasets for AI

We provide industries with training data for computer vision applications that sense and classify objects, recognize features and actions, and identify patterns and positions. We combine manual and AI methods to offer clients with computer vision training data suited to their models.

Computer Vision Datasets for Object Detection

COCO

COCO

COCO is a large-scale dataset that contains over 330K images and 2.5 million object instances. It is a diverse dataset that covers a wide range of objects and scenes.

PASCAL VOC

PASCAL VOC

PASCAL VOC designed for image classification and object recognition tasks. It is also used for object detection tasks, particularly for the development of object detection models for real-world applications.

ImageNet

ImageNet

Created for image classification tasks. However, it has also been used for object detection tasks, particularly for fine-tuning object detection models.

Open Images

Open Images

Large-scale dataset that contains over 9 million images and over 600K object instances. It is a diverse dataset that covers a wide range of objects and scenes, making it a popular choice for object detection tasks.

Key Capabilities of Our Computer Vision Data Annotation Experts

  • Well-versed with the requirements of your computer vision models & creating training data to meet your needs.
  • Familiarity with image annotation tools and techniques like bounding boxes, semantic segmentation, and object detection.
  • Knowledge of computer vision algorithms and deep learning techniques.
  • Ability to curate and clean large datasets to ensure the data is accurate and relevant for training.
  • Experience with data augmentation techniques to increase the size and diversity of the training data.

Types of Annotations in Computer Vision

Image Annotation

Image Annotation

We use image annotation to train, validate, and test machine learning algorithms with well-processed training data.

Discover more
Image

Video Annotation

We help you launch your AI projects successfully with well-refined video training data (frame-by-frame) that is accurately annotated & labeled.

Discover more
3D Point Cloud Annotation

3D Point Cloud Annotation

We accelerate AI integration into machine learning models with high-quality data labeling.

Discover more
AI Assisted Labeling

AI Assisted Labeling

We help expedite your AI projects with a robust semi-automated labeling approach that facilitates annotators with enhanced time and effort.

Discover more
ML Model Validation

ML Model Validation

We make your AI project successful with ML model validation techniques which check, validate, and rectify errors to fine-tune datasets thoroughly.

Discover more
Realtime Annotation Workflows

Realtime Annotation Workflows

We use concurrent labeling task distribution and queue system in the loop with production applications.

Discover more
Last Mile Automation

Last Mile Automation

We use AI in drones or robots to automate your last-mile operations to ensure your customers receive their products on time and in excellent condition.

Discover more
Time Series Data Labeling

Time Series Data Labeling

We expedite AI machinery and production operations like fault detection and usage-based pricing with the help of real-time analytics.

Discover more

Computer Vision Use Cases

Object Detection and Recognition

Object Detection and Recognition

Self-driving cars use this for recognizing and categorizing things like road signs or traffic signals, constructing 3D maps, and estimating motion. It plays a significant role in making self-driving cars a reality. These cars make use of sensors and cameras for collecting data about its environment, analyzing it, and responding appropriately.

Image Processing and Editing

Image Processing and Editing

This makes use of image processing techniques for stimulating vision on a human scale. For example, this technique can be used if the objective is to enhance the image for use in the future. However, it is called computer vision as its aim is to recognize objects which might include detecting an anomaly in the automatic driving system.

Facial Recognition

Facial Recognition

This involves using neural networks for detecting human face landmarks and to separate faces from other objects in an image. While computer vision is being implemented in face detection, neural networks are trained specifically for detecting human face landmarks separating faces from other objects in the image.

Medical Imaging

Medical Imaging

The healthcare industry utilizes of AI technologies for assisting doctors and nurses in a reliable manner. Advancements allow it to become an accurate source for usage in healthcare applications.

Video Surveillance

Video Surveillance

Since CCTV cameras are merely passive units, they assist in solving incidents, however, they are not effective in proactive prevention. AI and ML are instrumental in improving these cameras through a vast array of features which address recurrent issues with security.

Robotics

Robotics

Robotic vision comprises of algorithms, cameras, and other hardware which assist robots in developing visual insights. It permits machines in carrying out complicated visual tasks like robot arm which is programmed for picking up an object that’s placed on a board.

Augmented Reality

Augmented Reality

Augmented reality and virtual reality make use of computer vision for identifying and detecting real world objects and integrating virtual content on to them. This is called object detection and is a critical part of creating real and immersive AR experiences.

Drones

Drones

Monitoring of scene and environment using drones is a critical aspect of agricultural change. Farmers can utilize visual data taken using drone cameras and process them for remote monitoring of crops, livestock, collect information relating to field topography and also soil composition.

Retail and Advertising

Retail and Advertising

Computer vision is used in retail during self-checkout to analyze consumer interactions and tracking movement of goods, automatic replenishment for acquisition of picture data and carrying out an inventory scan by tracking objects on shelves at a fast pace, intelligent video analytics, and much more.

Why Cogito?

Years of Experience

11 Years of Experience

Expertise of Experts

Expertise of Experts

Flexible Payment Plan

Flexible Payment Plan

24x7 Support System

24×7 Support System

Talk to our Solutions Expert

    * Mandatory fields

    We're committed to your privacy. Cogito uses the information you provide to us to contact you about our relevant content, products, and services. For more information, check out our Privacy Policy.