Image Annotation

Get Your ML Training data to build better image, video, text and speech recognition with meaningful information that will be used to train and improve machine learning models.

We can use, bounding boxes, polygons, key points, and semantic segmentation to annotate image objects, features, and specific areas of interest accurately. Our image annotation services provide your models with quality and precise visual data.

High-Quality Custom Image

Your ML algorithms need a lot of datasets to effectively recognize videos. However, the video set that you require has to be the right volume and best match the requirements that are needed to carry out the training. Statswork offers a variety ofhigh-qualityvideo datasetsto match any specifications. Whether it’s gesture or motion detection or facial tracking data for advanced sentiment analyses, Statswork offers video datasets for any demographics in the form of individualized video sets to achieve outstanding results.This includes tracking human interactions, collecting license plates, motion sequences while doing different types of housework, gestures, sports activities, scenes, objects, animals, to watching an audience for signs of happiness.

2D bounding box Annotation

Draw rectangular boxes around objects to see both their location, and how big they are in an image—key for object detection.

3D bounding box Annotation

Uses cuboids to capture depth, orientation, and spatial volume of objects— this is mostly used for autonomous vehicle perception systems.

Semantic
segmentation

Labels each pixel in an image with a class to see what represents the scene and advance comprehension at the pixel level

Instance
segmentation

Like semantic segmentation but as a class of different object instances.

Polygon annotation

Encloses irregularly shaped objects with remarkable accuracy by outlining with multiple vertices—helps for detailed contours of objects.

Panoptic Segmentation

Semantic and instance segmentation combined so every pixel can be labelled, and every object can have a unique identifier, including the pixelation of overlapping instances in the same image.

OCR Text Annotation

Labelling and segmenting text within images for OCR tasks—used in document scanning and license plate detection techniques.

Image Classification

Tagging whole images with designed categories (eg “cat,” “vehicle,” “tumor”) to train classification models.

Object Tracking in Image Sequence

To develop techniques for predicting motion and tracking about an axis of relative translation, annotating and linking in the same image, in multiple frames, the same object.

Image Tagging / Attribute Annotation

To add descriptive tags or attributes to images (i.e. weather, color, feelings) to create richer datasets for multi-label learning.

Landmark & Key point Annotation

Marking important points in objects (like facial landmarks, body joints, or mechanical components) for objective pose estimation and motion analysis.

Medical Image Annotation

specialised annotation of medical imaging like X-Rays, MRIs, or CT scans from health domain experts relevant to the development of healthcare-focused AI.

Multispectral & Satellite Image Annotation

Annotating aerial or satellite images including ND

Polyline and line annotation

Used to markup boundaries, lanes, roads, pipelines, and other linear features frequently implemented

Thanks to the precise medical image annotation provided by the team, our AI model achieved clinical-grade accuracy. This directly contributed to our publication in the Journal of Medical Imaging and Health Informatics.

— CTO, HealthTech AI Startup,

- USA

We were impressed by the team's expertise in clinical text annotation. Their work helped us build an NLP pipeline that led to our successful article in the International Journal of Medical Informatics.

Lead Researcher, Clinical Research Organization,

- UK

The annotated dataset they delivered met all journal standards, and their adherence to HIPAA compliance was commendable. Our study was published in the BMC Medical Informatics and Decision-Making journal.

Principal Investigator, Healthcare AI Lab,

- Canada

The Statswork team helped us annotate and label a massive dataset for drug discovery, contributing to our manuscript accepted in Frontiers in Pharmacology. Their scientific accuracy was outstanding

Senior Scientist, Pharma Research Unit,

- India

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Image Annotation

High-Quality Custom Image

— CTO, HealthTech AI Startup,

Lead Researcher, Clinical Research Organization,

Principal Investigator, Healthcare AI Lab,

Senior Scientist, Pharma Research Unit,

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