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 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 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 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.
Created for image classification tasks. However, it has also been used for object detection tasks, particularly for fine-tuning object detection models.
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
We use image annotation to train, validate, and test machine learning algorithms with well-processed training data.
We help you launch your AI projects successfully with well-refined video training data (frame-by-frame) that is accurately annotated & labeled.
3D Point Cloud Annotation
We accelerate AI integration into machine learning models with high-quality data 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.
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.
Realtime Annotation Workflows
We use concurrent labeling task distribution and queue system in the loop with production applications.
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.
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.
Computer Vision Use Cases
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
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.
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.
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.
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.
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 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.
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
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.