Image Annotation Services
Supercharge your AI Training Data with Shaip’s Image Annotation Services for Computer Vision
Train AI models with super-precise Image Annotation & Image Tagging Services
All advanced computing systems based on computer vision require airtight training data for accurate results. Regardless of which industry or market segment you’re into, your AI-driven product will fail to yield desirable results if you don’t train it right. That is exactly where image labeling comes in. This is an inevitable process that makes your AI’s results more accurate, relevant and bias-free by annotating or tagging all the elements in an image.
In an image of a restaurant, your machine learning module would learn what tables, plates, food, cutlery, water and more are and precisely differentiate each in images once it starts training with the right data. For that to happen, thousands of objects in an image have to be labeled meticulously by experts. At Shaip, we have industry pioneers who have been working on image labeling for decades. From conventional images to highly-niche medical data, we can annotate them all.
Image Annotation Tool
We have one of the most advanced image labeling tool in the market that makes image labeling precise and super-functional. Besides, it also makes dynamic scalability possible. No matter if your project requires complex datasets, has a limited time to market, or razor-sharp annotation mandates, we can deliver with our proprietary image labeling platform.
However, not all projects dictate the implementation of the same image labeling technique. Every project is unique in terms of its requirements & use case and only case-specific techniques work for the most accurate outcomes.
Image Annotation Companies, such as Shaip, deploys diverse labeling techniques after carefully studying project scope and requirements. Depending on your machine learning project, we would work on one or a combination of these image annotation techniques:
Types of Image Annotation
Image Annotation Techniques – We Master
The various types of Annotation are as follows
The most commonly used image labeling technique in computer vision is bounding box annotation. In this technique, boxes are manually drawn over image elements for easy identification
Similar to bounding box but the difference is, annotators, draw 3D cuboids over objects to specify 3 important attributes of an object – length, depth, and breadth.
In this technique, every pixel in an image is annotated with information and separated into different segments you need your computer vision algorithm to recognize.
In this technique, irregular objects are marked by plotting points on each vertex of the target object. It allows all of the object's exact edges to be annotated, regardless of its shape
In this technique, the labeler needs to label key points at specified locations. Such labels are commonly used where anatomical elements are labeled for facial & emotion detection
In this technique, annotators draw straight lines to classify that element as a particular object. It helps establish boundaries, define routes or pathways, etc.
Image Annotation Process
Transparency lies at the core of our collaboration. Our stringent operating and fluid communication mechanisms ensure a rewarding collaboration.
Dedicated and trained teams:
- 7000+ collaborators for Data Collection, Labeling & QA
- Credentialed Project Management Team
- Experienced Product Development Team
- Talent Pool Sourcing & Onboarding Team
Highest process efficiency is assured with:
- Robust 6 Sigma Stage-Gate Process
- A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
- Continuous Improvement & Feedback Loop
The patented platform offers benefits:
- Web-based end-to-end platform
- Impeccable Quality
- Faster TAT
- Seamless Delivery
We annotate & label a variety of images for different industries
Computer vision is dynamically becoming universal with tons of newer use cases cropping up every single day. It’s the only way companies gain an edge in the market. That’s why we extend our high quality image labeling services to requirements from across diverse industries. We cater to industries such as:
For gesture recognition, ADAS features, Level and 5 autonomy
For road mapping, crack detection and ODAI (Object Detection Aerial Imagery)
For inventory management, supply-chain management, gesture recognition, & more
For semantic understanding, facial recognition, advanced object tracking, and more
For weed and disease detection and crop identification
Fashion & Ecommerce
For image categorization, image segmentation, image classification, object detection and multi-label classification
Image Annotation Outsourcing – Why Choose Shaip
Our pool of experts who are proficient in image labeling can procure accurate and effectively annotated datasets.
Focus on growth
Our team helps you prepare image data for training AI engines, saving valuable time & resources.
Our team of collaborators can accommodate additional volume while maintaining the quality of data output.
As experts in training, and managing teams, we ensure projects are delivered within the defined budget.
Multi-Source/ Cross-Industry capabilities
The team analyzes data from multiple sources & is capable of producing AI-training data efficiently and in volumes across all industries.
Stay ahead of the competition
The wide gamut of image data provides AI with copious amounts of information needed to train faster.
Expert image data collection isn’t all-hands-on-deck for comprehensive AI setups. At Shaip, you can even consider the following services to make models way more widespread than usual:
We specialize in making textual data training ready by annotating exhaustive datasets, using entity annotation, text classification, sentiment annotation, and other relevant tools.
Labeling audio sources, speech, and voice-specific datasets via relevant tools like speech recognition, speaker diarization, emotion recognition, and more, is something Shaip specializes in.
Shaip offers high-end video labeling services for training Computer Vision models. The aim here is to make datasets usable with tools like pattern recognition, object detection, and more.
Image Annotation & Labeling for Computer Vision
Computer vision is all about making sense of the visual world to train computer vision applications. Its success completely boils down to what we call image annotation – the fundamental process behind the technology that makes machines make intelligent decisions and this is exactly what we are about to discuss and explore.
Computer Vision Services & Solutions
Computer vision is an area of Artificial Intelligence technologies that train machines to see, understand, and interpret the visual world, the way humans do. It helps in developing the machine learning models to accurately understand, identify, and classify objects in an image or a video – at a much larger scale & speed.
Data Annotation & Data Labeling
So, you want to start a new AI/ML initiative and are realizing that finding good data will be one of the more challenging aspects of your operation. The output of your AI/ML model is only as good as the data you use to train it – so the expertise you apply to data aggregation, annotation, and labeling is of critical importance.
Get professional, scalable, & reliable image annotation services. Schedule a Call Today…
Image annotation is the process of annotating an image with predetermined labels to give the computer vision model information about what is shown in the image with the help of expert human annotators. In short it is all about adding metadata to a dataset, which makes specific objects recognizable for AI engines. Tagging objects within images makes it informative and meaningful for machine learning algorithms to interpret the labeled data, and get trained to solve real-life challenges.
For systems reliant on computer vision, what is fundamental is image labeling/annotation. It is because of this process that an autonomous car can differentiate between a mailbox and a pedestrian, the red light and the green light, and more; in order to make appropriate driving decisions. For an image recognition system to be powerful, it has to process millions of images to precisely understand different objects in a segment it is intended to be implemented for.
Image annotation trains AI and ML models for computer vision by facilitating training that concerns object and boundary detection and image segmentation.
The different image annotation techniques consists of:
- Bounding Boxes
- 3D Cuboids
- Semantic Segmentation
- Polygonal Annotation
- Image Categorization
- Landmark Annotation
- Line Segmentation
Manual image annotation is a good strategy for training unsupervised ML models and algorithms, in regard to computer vision, as these models aren’t capable of detecting, finding, and identifying images on their own. Also, manual labeling concerns describing the image regions, textually. Automatic annotation is meant for more intelligent and pre-trained setups with a focus on linguistic indexing, and auto metadata assigning.
Also, manual image labeling, despite being slower, is better equipped in handling project variability, and scalable needs.
An image annotation tool is a resource that uses a balance of computer-assisted effort and manual exertion to label images before feeding them into the models
You can annotate an image by subjecting it to a wide range of techniques like Bounding Boxes, Cuboids, Polygon annotation, line segmentation, landmark annotation, and more. Once the technique sits with the image, the same can be fed into the system.
The possible industry use cases are:
- Autonomous vehicles for gesture recognition, ADAS features, Level and 5 autonomy
- Drones for road mapping, crack detection and ODAI (Object Detection Aerial Imagery)
- Retail for inventory and shelf management, supply-chain management, gesture recognition and more
- AR/VR for semantic understanding, facial recognition, advanced object tracking and more
- Agriculture for weed and disease detection and crop identification
- And Fashion and eCommerce for image categorization, object detection and multi-label classification