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.

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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 is 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

ImageNet was 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

Open Images is a 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.

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Video Annotation

Video Annotation

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

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3D Point Cloud Annotation

3D Point Cloud Annotation

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

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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.

Image Processing and Editing
Image Processing and Editing

This makes use of image processing techniques for stimulating vision on a human scale. It can enhance the image for use in the future.

Facial Recognition
Facial Recognition

This involves using neural networks for detecting human face landmarks and separating faces from other objects in an image.

Medical Imaging
Medical Imaging

The healthcare industry utilizes AI technologies for assisting doctors and nurses reliably.

Video Surveillance
Video Surveillance

AI and ML improve cameras through features that enhance security and monitoring.

Robotics
Robotics

Robotic vision comprises algorithms and hardware that assist robots in developing visual insights.

Augmented Reality
Augmented Reality

AR and VR use computer vision for identifying and detecting real-world objects and integrating virtual content.

Drones
Drones

Drones monitor scenes and environments, providing critical visual data for agriculture.

Retail and Advertising
Retail and Advertising

Computer vision aids in self-checkout, analyzing consumer interactions and tracking goods.