Faster catalogue tagging
Turn hours of manual garment tagging into near-instant, consistent attribute data for every product image.
AI Apparel Detection
Train custom AI to detect garments, read colours, patterns and fabrics, and tag every look in images and video, so catalogues, search and retail operations run on structured fashion data.
Aarvexa AI at a glance
- 0+Years of engineering excellence behind every model.
- 0+Projects delivered across industries.
- CustomGarment classes trained around your catalogue.
Looks tagged today
- tops482
- bottoms311
- footwear206
AttributesColour · Pattern · Fabric · Fit · Neckline
4 garments segmentedImpact
Where fashion and retail teams typically see gains once detection is trained on their own garments. Actual results vary by project.
Better product discovery
Rich colour, pattern, fit and style attributes make search, filters and recommendations far more relevant.
Less manual labelling
Let the model handle repetitive identification so merchandising teams focus on creative decisions.
Sharper trend visibility
See which garments, colours and styles appear across images and video, and how that mix shifts over time.
Note: Results vary according to image quality, garment variety, background and lighting conditions, deployment environment, and the accuracy requirements established for each apparel detection solution.
Services
Garment Detection
Locate and label tops, bottoms, dresses, outerwear, footwear and accessories in product photos, street imagery, lookbooks and video frames.
Attribute Recognition
Read colour, pattern, fabric, fit, sleeve length, neckline, length and other visual attributes that describe each garment.
Automated Catalogue Tagging
Generate consistent product tags and metadata at scale so new arrivals reach your store faster, with fewer manual errors.
Visual Style Search
Let shoppers upload a photo and find visually similar garments, matching items and complementary pieces from your catalogue.
Virtual Try-On Support
Segment garments and body regions so try-on and fit-preview experiences can place clothing accurately on a person.
Retail Video Analytics
Process in-store and event footage to recognise which garments are being tried, carried, worn or left behind, in aggregate and privacy-aware.
Uniform and Dress-Code Checks
Verify that required uniform items, safety clothing or branded apparel are being worn in workplaces and venues.
Capabilities
Apparel detection software transforms images and video into structured fashion data for identifying, classifying, describing and matching garments.
Detect garments
Find every garment and accessory in an image or frame despite changing poses, folds, layering, occlusion, lighting or backgrounds.
Classify clothing types
Assign each detected item to your own taxonomy, from broad categories to fine-grained styles such as bomber, wrap dress or straight-leg jeans.
Recognise attributes
Extract dominant colours, patterns, textures, fabrics, necklines, sleeve types and lengths as structured, searchable data.
Associate garments with a look
Group the items worn together into one outfit, so styling, uniform and look-level analysis work on complete looks, not isolated pieces.
The Problem
Off-the-shelf clothing recognition works for common categories, but often lacks the accuracy, flexibility and ownership that fashion brands and retailers need.
Fine-grained gaps
Generic detectors recognise “shirt” or “dress” but miss the nuance fashion needs: cuts, silhouettes, prints, trims, layering and seasonal styles.
Integration wall
Standalone fashion APIs rarely connect to your PIM, e-commerce platform, merchandising tools, mobile app or retail systems out of the box.
Trend drift
Fashion changes every season. Static public models fall behind new silhouettes, colours and collections unless they are retrained on your catalogue.
No ownership
Third-party APIs create vendor dependency, limit customisation and keep your labelled data and model improvements out of your control.
Technology
These apparel recognition solutions combine detection, segmentation and fine-grained classification, selected according to garment complexity and deployment requirements.
Garment object detection
Deep learning detectors locate each garment and accessory and return labels, confidence scores and bounding-box coordinates.
Instance segmentation
Pixel-level masks separate each garment from the body and background, enabling precise measurement, recolouring and try-on.
Fine-grained classification
Neural networks trained on your taxonomy distinguish similar styles, cuts and sub-categories that generic models confuse.
Multi-label attribute recognition
Predict several attributes at once, such as colour, pattern, fabric, sleeve and neckline, for every detected garment.
Pose and landmark estimation
Locate shoulders, waist, hem and other landmarks to understand fit, drape and garment placement on the body.
Colour extraction
Identify dominant and secondary colours, normalised for lighting, so catalogues can be filtered by true colour.
Pattern and texture analysis
Recognise stripes, checks, florals, prints, knits and textures that describe how a garment actually looks.
Logo and brand-mark detection
Detect logos, labels and brand marks on garments for placement checks, authenticity workflows and brand-compliance review.
Person-to-garment association
Link detected garments to the correct wearer in a frame to understand complete outfits in group scenes.
Style embeddings
Convert garments into vectors so visually similar items, matching pieces and style-based recommendations can be retrieved instantly.
Video tracking
Follow garments across video frames and camera views to count appearances, measure engagement and keep stable identities.
Virtual try-on mapping
Warp and align garment masks to body regions so shoppers can preview fit and style before they buy.
Compare
See where a model trained on your own garments pulls ahead of off-the-shelf fashion APIs and hand-tagging.
CapabilityAarvexa custom modelOff-the-shelf APIManual tagging
Fine-grained styles and attributesYes~PartlyYes
Consistent across the whole catalogueYesYesNo
Learns new seasonal collectionsYesNoYes
Connects to your PIM and storeYes~PartlyNo
Predictable cost at high volumeYesNoNo
You own the model and dataYesNoYes
Tags images in seconds, not hoursYesYesNo
Tech Stack
Production-ready apparel detection combines vision frameworks, scalable infrastructure, flexible deployment and connected business systems.
Frameworks
- TensorFlow and PyTorch
- OpenCV and YOLO
Deployment
- Cloud, edge, and on-premises
- Mobile and hybrid models
Infrastructure
- AWS, Azure, and GCP
- GPU-optimised pipelines
Integration
- REST APIs and PIM systems
- E-commerce and retail platforms
Process
A five-step delivery process connects your fashion requirements, image data, model development, integration and long-term optimisation in one structured engagement.
Discovery call
Define your garment taxonomy, image sources, target attributes, workflows, performance expectations, deployment preferences and measurable outcomes.
Data assessment
Review existing product images and footage, identify gaps, check image quality, define labelling guidelines and confirm technical feasibility.
Model development
Train custom garment and attribute classes, test model architectures, set accuracy benchmarks and optimise on representative catalogue and store images.
Integration and deployment
Connect the apparel detection software to your catalogue, search, apps, cameras and business systems across your chosen infrastructure.
Continuous optimisation
Monitor accuracy, retrain on new collections, add garment classes, handle seasonal drift and keep improving with production data.
Engagement
Choose an engagement model based on your catalogue size, available data, operational complexity and intended deployment scale.
Proof of concept
Validate apparel detection feasibility, available data, model accuracy and technical risks through a focused proof of concept.
Phase 1Pilot deployment
Deploy the solution on a selected product range, store or camera set, with a limited group of users, to measure real-world value.
Phase 2Enterprise rollout
Expand the validated solution across catalogues, brands, stores, channels and business systems through a structured enterprise deployment.
Phase 3Pricing
Apparel detection software costs depend on garment variety, training requirements, image sources, integrations, deployment environments, performance targets and ongoing optimisation needs.
Cost factors
Pricing is influenced by garment taxonomy size, attribute depth, image volume, processing speed, accuracy targets, integrations and deployment needs.
Cloud API pricing
Cloud APIs charge per image or processing volume, which makes long-term costs hard to predict as catalogues and traffic grow.
Custom development
Custom development uses project-based pricing aligned to capabilities, integration scope, ownership expectations and measurable business outcomes.
Long-term value
Owned models remove recurring per-call charges, reduce vendor dependency and deliver better economics as volumes increase.
Industries
These apparel recognition solutions help organisations automate tagging, search, monitoring and decision-making across fashion, retail and operations.
Fashion e-commerce
Auto-tag new arrivals, power visual search and filters, and improve recommendations with consistent garment and attribute data.
Retail and merchandising
Monitor racks and displays, spot size and style gaps, and understand which garments customers pick up, try and buy.
Fashion design and trends
Analyse runway, social and street imagery to see rising silhouettes, colours and prints before they reach the shelf.
Returns and warehouse operations
Sort, grade and re-list returned apparel faster by recognising garment type, condition and visible defects at intake.
Workforce and uniform compliance
Check uniforms, branded apparel and protective clothing across hospitality, healthcare, logistics and field teams.
Media and content tagging
Tag clothing in video and image libraries so editors, advertisers and platforms can search, curate and moderate content.
Automated Garment Tagging
Garment Tagging ImpactSample view
Manual tagging effortReduced
Garment and attribute detectionAutomated
Catalogue consistencyImproved
Apparel Tagging Case Study · Sample
Automated garment tagging for an online fashion catalogue
An online fashion retailer needed consistent garment categories, colours and attributes across thousands of new product images. Aarvexa AI builds a custom apparel detection pipeline that tags every image automatically and feeds clean metadata into search and filters.
Garment and Attribute Detection- Image-levelGarment detection
- LessManual tagging effort
- CleanerCatalogue metadata
- Built an upload-and-tag workflow for new product photography.
- Detected garments and read colour, pattern, fabric and fit for each item.
- Mapped results to the retailer's own taxonomy and product fields.
- Reduced repetitive tagging while improving search and filter quality.
Custom fashion intelligence
Style intelligenceRecognise cut, print, fabric, colour and occasion.
Look intelligenceUnderstand outfits, layering and styling relationships.
Built around your collections
Turn More of Your Fashion Imagery Into Business Intelligence
Extend apparel detection with custom models, tracking, outfit understanding and analytics tailored to your catalogue, stores and merchandising workflows.Do more with computer vision
Build on your existing apparel recognition with custom computer vision models designed around your garments, imagery, accuracy goals and merchandising workflows. Add deeper layers of intelligence that explain style, attributes, outfits and movement over time.
Cross-Camera Garment Tracking
Keep garment identities stable as items move between frames, fitting rooms, racks and camera views.
Fine-Grained Style Attributes
Recognise cut, silhouette, print, trim, fabric, season and occasion beyond basic category and colour.
Outfit and Context Understanding
Understand which pieces are worn together, how they are layered and what styling rules a look follows.
Trend and Inventory Analytics
Aggregate detections into trend, assortment and stock insights that guide buying and merchandising.
Explore Computer Vision DevelopmentDevelop capabilities tailored to your garments, image sources, stores and operational requirements.
Why Aarvexa
Organisations choose Aarvexa AI for custom apparel detection that combines specialised model development with scalable engineering, integration, deployment and optimisation.
Computer vision experience
Aarvexa teams combine deep software engineering experience with specialised capability in computer vision, machine learning, data engineering, cloud and integration.
Experienced engineers
Work with AI engineers skilled in model architecture, training, validation, optimisation, data pipelines and production computer vision deployment.
Scalable systems
Solutions are designed for reliability as catalogues, image volumes, collections, stores and channels grow.
Custom models
Your apparel AI is trained on your garments, taxonomy and business rules rather than generic public labels.
Flexible deployment
Deploy through cloud, edge, on-premises, mobile or hybrid infrastructure while meeting performance, privacy and security needs.
Ongoing optimisation
Aarvexa teams monitor performance, retrain for new collections, add garment classes and adapt the solution as fashion changes.
More Solutions
Extend visual intelligence across object recognition, visual search, image analysis, tracking, safety monitoring, food recognition and structural inspection.
Object Recognition Software
Train custom recognition for your products, equipment and assets across images and video.
AI Visual Search
Let users search with an image and instantly find visually similar products, assets or content.
AI Image Analysis Software
Extract objects, attributes and insights from images with custom computer vision models.
Object Tracking Software
Track objects across video frames to monitor movement, position and activity over time.
PPE Detection
Detect helmets, vests, masks, gloves and other protective equipment in workplaces.
Food Recognition API
Recognise dishes, ingredients and meal components from uploaded or captured images.
AI Crack Detection
Identify surface cracks and structural defects with automated visual inspection.
FAQ
Straight answers on apparel detection accuracy, technology, deployment, pricing and who we are.
It is computer vision that finds garments and accessories in images and video, classifies them and reads attributes such as colour, pattern and fabric. Aarvexa trains it around your own catalogue and taxonomy.
Auto-tag product images, power visual search and filters, support virtual try-on, monitor racks and uniforms, grade returns and turn fashion imagery into trend and assortment insight.
Generic APIs recognise common categories. A custom model learns your exact styles, attributes and seasonal collections, connects to your systems and stays under your ownership.
Book a discovery call. We define your garment taxonomy, image sources and outcomes, then assess your data before any development begins.
Fashion e-commerce, retail and merchandising, fashion design and trend analysis, returns and warehouse operations, workforce uniform compliance and media tagging.