What is a vision language model and how does it help retail operations?

A vision language model (VLM) processes shelf images and answers questions about them in natural language. Instead of counting individual SKUs with barcodes, you ask the model 'Which products are out of stock on the top shelf?' and it responds with specific product names and locations. Datature Vi fine-tunes these models on your store's specific planogram layouts.

How do VLMs compare to traditional planogram compliance tools?

Traditional tools match shelf photos against a reference image pixel by pixel. They break when lighting changes, products shift slightly, or new SKUs appear. Datature Vi trains a VLM that understands shelf layout conceptually: it recognizes products by appearance and context, not pixel coordinates. This makes compliance checks reliable across stores and lighting conditions.

Why should retailers fine-tune a VLM with Datature Vi instead of using generic image recognition?

Generic models cannot distinguish between your private-label products, recognize your planogram conventions, or understand store-specific shelf layouts. Datature Vi fine-tunes on your annotated shelf images so the model learns your exact product catalog, acceptable facing counts, and display standards.

How much shelf imagery does Datature Vi need for training?

Start with 200 to 400 annotated shelf images covering your primary product categories and store formats. Datature Vi's IntelliScribe pre-labeling generates captions and links product descriptions to bounding boxes 3-5x faster than manual annotation. Include images from multiple stores and lighting conditions for reliable results.

Can Datature Vi detect out-of-stock products on shelves?

Yes. Train phrase grounding annotations that describe expected product positions and VQA pairs that answer stock-level questions. The model learns to identify empty shelf space, misplaced products, and low stock conditions. Datature Vi's guided JSON output structures these findings into actionable restocking alerts.

Which base model is best for retail shelf analysis on Datature Vi?

Qwen2.5-VL 7B handles the fine-grained visual detail needed for distinguishing similar product packages. Its 128K context length processes high-resolution shelf panoramas without cropping. LoRA training on Datature Vi keeps training fast and memory-efficient on a single A100 GPU.

Can Datature Vi audit promotional display compliance?

Yes. Annotate your promotional display standards using phrase grounding (linking descriptions like 'end cap promotional signage' to visual locations) and VQA (Q: 'Is the promotional pricing label visible?' A: 'No, the label is missing from the second shelf'). Datature Vi fine-tunes the model to check every promotional element you define.

How does Datature Vi deploy for in-store shelf auditing?

The simplest path: store associates snap a shelf photo on their phone, and Vi's cloud inference API returns the audit results in seconds. No local GPU or special hardware needed. For large chains that want continuous monitoring, deploy the Vi SDK on a local server connected to fixed aisle cameras.

Can Datature Vi process shelf images from mobile phones?

Yes. Snap a photo with any smartphone, send it to Vi's cloud inference API, and get shelf audit results back in seconds. Models fine-tuned on Datature Vi handle the variable angles, distances, and lighting typical of phone-captured shelf photos. Include representative phone-captured images in your training dataset for the best results.

How does Datature Vi measure shelf audit model accuracy?

Datature Vi provides F1 score, precision, and recall for product detection, plus BLEU and BERTScore for natural language shelf descriptions. The side-by-side evaluation view compares model predictions against ground truth annotations, and the checkpoint scrubber shows how accuracy improves across training epochs.

Can Datature Vi count product facings on a shelf?

Yes. Train VQA annotations where questions ask about facing counts (Q: 'How many facings of Product X on the second shelf?' A: '3 facings'). Datature Vi's phrase grounding also locates each individual facing with a bounding box for visual verification.

How does Datature Vi handle private-label and store-brand products?

Fine-tuning is the key advantage here. Generic models often confuse similar-looking private-label products. Datature Vi trains on your annotated shelf images that include your private-label SKUs, so the model learns the specific packaging differences that matter for your audit requirements.

Retail Shelf Audit AI with VLMs

THE CHALLENGE

THE PROBLEM.

Retailers lose 4% of revenue to out-of-stock items. Manual shelf audits are infrequent and inconsistent. Planogram compliance drops 15-20% between audits, and each missed gap is a lost sale.

Percentage of total retail revenue lost annually to out-of-stock items that customers cannot find

15-20%

Planogram compliance degradation that occurs between scheduled manual shelf audit walkthroughs

Real-time planogram compliance rate tracked continuously through automated shelf image analysis

No Domain KnowledgeCan't Read ImagesFine-Tuned on Vi

THE BASELINE

GENERAL MODELS LACK DOMAIN EXPERTISE.

GPT-4o, Claude, and Gemini have broad knowledge, but zero understanding of your specific domain, standards, or terminology.

✗No actionable shelf intelligence

Why it fails:

•Detects large empty gaps but misses 2-3 of 4 out-of-stock facings, especially partial empties

•Identifies major national brands but confuses visually similar products, store brands, and sub-SKU variants

Foundation Model (GPT-4o / Claude / Gemini)

You

Prompt

Analyze this retail shelf image. Identify out-of-stock positions and misplaced products.

image_001.jpg

Response

This image shows a retail store shelf with various products displayed across multiple shelves. The shelves appear to be reasonably well-stocked with a mix of different product categori

THE GAP

GENERAL MODELS CAN'T READ YOUR IMAGES.

Even with reference documents attached, foundation models cannot reliably interpret domain-specific visual data.

✗Spots gaps but wrong positions

Why it fails:

•Identifies 1 of 4 out-of-stock positions (25% recall)

•"Second shelf, center area" is wrong (actual gaps are shelf 3 left, shelf 4 right)

Foundation Model + RAG (Context-Augmented)

You

Prompt + Reference Data

Audit this retail shelf for out-of-stock and planogram compliance

image_001.jpg

store_planogram_layout.pdf25_shelf_reference_images.zip

Response

Comparing to the planogram, the shelf appears to have most products in roughly the correct positions. I notice what might be an empty slot on the second shelf from the top

THE ANSWER

YOUR DATA, FINE-TUNED ON VI.

A model trained on your private data sees exactly what you see. Your domain. Your standards. Production-ready.

✓Store-ready. Restocking alerts in real time.

95.4%

OOS Detection

93.8%

SKU Match

110ms

Latency

Datature Vi — Inference

LIVE

Model: ft-shelf-audit-internvl8b-v3

MISPLACED_ITEM · 95.0%

OUTDATED_LAYOUT · 95.0%

INCORRECT_LAYOUT · 95.0%

Vi Output

Shelf section: Sauces & Condiments. 5 issues detected. MISPLACED_ITEM: Row 3 center — product does not match planogram position, wrong category placement. INCORRECT_LAYOUT (x3): Row 2 left, Row 3 left, Row 1 right — facing arrangement does not match planogram spec. OUTDATED_LAYOUT: Row 4 bottom section — shelf tags and arrangement inconsistent with current planogram version. Planogram compliance: 72%. Action: restocking and rearrangement required.

95.4%

OOS Detection

93.8%

SKU Match

110ms

Latency

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SEE IT IN ACTION

YOUR OUTPUT, YOUR FORMAT.

Structured reports, raw JSON, concise alerts. Control the output with system prompts and refine it with RLHF. The model speaks the way your application needs it to.

Generate a shelf compliance report for this aisle image with stock levels and restock recommendations

FAQ

SHELF & PLANOGRAM AUDIT
FAQ.

Everything you need to know about using Datature Vi for Shelf & Planogram Audit.

GET STARTED

SEE IT
IN ACTION.

30-minute walkthrough of Datature Vi applied to Shelf & Planogram Audit. Bring your own dataset or use ours.

Schedule a Demo

Walk through the full pipeline with an engineer. Annotation, training, evaluation, and deployment for your specific use case. 30 minutes.

Start Free

3,000 data rows and 300 compute credits free every month. All annotation modes, all model architectures, Vi SDK access. No credit card.

All annotation modes included

Qwen2.5-VL, InternVL3.5, Cosmos

Vi SDK with 4-bit quantization

Get Started

Enterprise Ready

View Trust Center

SOC 2 Type II

Audited annually

HIPAA Compliant

PHI safeguards

AES-256 + TLS 1.2+

Encrypted at rest and in transit

G2 High Performer

4.9/5 with 47 reviews

Your Data, Your Models

Full ownership and export

AUTOMATED SHELF COMPLIANCE AUDITING

THE PROBLEM.

GENERAL MODELS LACK DOMAIN EXPERTISE.

GENERAL MODELS CAN'T READ YOUR IMAGES.

YOUR DATA, FINE-TUNED ON VI.

FROM RAW IMAGES TO
PRODUCTION MODEL.

Label Products and Positions

Train on Your Store Layout

Monitor Continuously

YOUR OUTPUT, YOUR FORMAT.

AUDIT SHELVES WITHOUT NEW HARDWARE.

SHELF & PLANOGRAM AUDIT
FAQ.

SEE IT
IN ACTION.

Schedule a Demo

Start Free

RELATED USE CASES.

Warehouse Intelligence

Quality Inspection

Document Extraction

TRY THIS USE CASE.
START FREE.

AUTOMATED SHELF COMPLIANCE AUDITING

THE PROBLEM.

GENERAL MODELS LACK DOMAIN EXPERTISE.

GENERAL MODELS CAN'T READ YOUR IMAGES.

YOUR DATA, FINE-TUNED ON VI.

FROM RAW IMAGES TOPRODUCTION MODEL.

Label Products and Positions

Train on Your Store Layout

Monitor Continuously

YOUR OUTPUT, YOUR FORMAT.

AUDIT SHELVES WITHOUT NEW HARDWARE.

SHELF & PLANOGRAM AUDITFAQ.

What is a vision language model and how does it help retail operations?

How do VLMs compare to traditional planogram compliance tools?

Why should retailers fine-tune a VLM with Datature Vi instead of using generic image recognition?

SEE ITIN ACTION.

Schedule a Demo

Start Free

RELATED USE CASES.

Warehouse Intelligence

Quality Inspection

Document Extraction

TRY THIS USE CASE.START FREE.

FROM RAW IMAGES TO
PRODUCTION MODEL.

SHELF & PLANOGRAM AUDIT
FAQ.

SEE IT
IN ACTION.

TRY THIS USE CASE.
START FREE.