Computer vision datasets for warehouse automation
From pallet detection to package sorting, images.cv helps logistics teams build structured datasets for warehouse CV systems — with annotation, packaging, and ML-ready delivery.
Warehouse CV use cases
Structured datasets for the most common warehouse computer vision tasks.
Pallet detection and counting
Detect, count, and classify pallets across warehouse zones for automated inventory tracking.
Package and parcel recognition
Identify packages by size, shape, and condition for sorting and damage detection pipelines.
Forklift and vehicle tracking
Track forklifts, AGVs, and personnel for safety monitoring and path optimization.
Shelf and rack inventory monitoring
Monitor shelf occupancy and stock levels with detection models trained on rack imagery.
Loading dock automation
Detect trailer positions, door states, and loading activity for dock scheduling systems.
Barcode and label detection
Locate barcodes, QR codes, and shipping labels on packages for automated scanning workflows.
What you get
Every warehouse dataset ships as a structured, annotated package ready for your training pipeline.
bboxes/
Bounding box JSON files
coco/
COCO-format annotations
data/
Final image files
masks/
Segmentation masks
yolo/
YOLO TXT annotations
index.csv
File-level dataset index
meta.json
Dataset metadata summary
Standard annotation formats
YOLO, COCO, bounding boxes, and segmentation masks in every delivery.
Train / validation / test split
Optional dataset splitting with consistent file naming across partitions.
Warehouse-specific labeling
Category labels tailored to your warehouse environment — pallets, vehicles, racks, packages, and more.
Tell us about your dataset needs
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Annotation formats needed
Ready to build your warehouse CV dataset?
Tell us about your warehouse automation use case and we'll scope a dataset that fits.