Custom image datasets for ML teams
From raw images or generation to ML-ready delivery. images.cv helps teams create structured computer vision datasets with annotation, packaging, and scalable workflow support.
Why teams use images.cv
Dataset production shouldn't require a dedicated infrastructure team.
Turn raw images into ML-ready datasets
Provide your images — we handle annotation, structuring, and packaging so your team can focus on model training.
Generate custom visual data for narrow CV use cases
Synthetic dataset production for objects, industrial parts, products, and other non-human categories.
Deliver COCO, YOLO, masks, and structured outputs
Every dataset ships in standard annotation formats with consistent folder layouts — no custom parsing needed.
Skip building dataset ops infrastructure
Annotation pipelines, quality checks, format conversions, and delivery — handled without adding headcount.
What we can help with
End-to-end dataset workflows — from raw input to structured, annotated delivery.
Customer-owned images to dataset
Upload or provide raw images
We prepare training-ready output
Structured annotations and metadata
Annotation and packaging
Bounding boxes and segmentation masks
COCO and YOLO format exports
Dataset indexing and metadata
Custom synthetic dataset production
Object, industrial, product, and retail categories
Warehouse, manufacturing, and non-human subjects
Built for training and experimentation
Structured delivery for ML workflows
Train-ready file organization
Metadata and dataset index files
Optional train / val / test split support
Tell us about your dataset needs
Fields marked with * are required.
Annotation formats needed
What you get
A structured, annotated dataset 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.
Annotation alignment
All annotations are validated against image files to ensure consistency.
Batch delivery
Large datasets can be delivered in structured batches with incremental updates.
How the workflow works
A practical, repeatable process from first conversation to ML-ready delivery.
01
Define scope
Align on category, volume, annotation types, and delivery format.
02
Align dataset spec
Finalize the exact structure, naming, and output requirements.
03
Produce or prepare data
Generate synthetic images or process customer-provided photos.
04
Validate and package
Quality checks, annotation alignment, and structured packaging.
05
Deliver ML-ready dataset
Consistent folder layout, metadata, and ready-to-train output.
Built for real computer vision workflows
Teams across industries use custom datasets to power production CV systems.
Structured dataset delivery in production
We already support structured labeled dataset workflows in real B2B contexts. The exact category may vary, but the production logic is the same: define the spec, produce or prepare the data, validate outputs, and deliver it in a format ML teams can use immediately.
Frequently asked questions
Common questions about B2B dataset workflows.
Do you support YOLO and COCO?
Yes. We deliver YOLO TXT annotations, COCO JSON, bounding boxes, and segmentation masks — all in a consistent folder layout ready for training pipelines.
Can you provide segmentation masks?
Yes. Pixel-level segmentation masks are a standard part of our delivery, alongside bounding boxes and structured annotation files.
Can you help with custom dataset specs?
Absolutely. We work with your team to define the exact category, annotation types, naming conventions, and folder structure before production begins.
Do you support synthetic dataset creation?
Yes. Synthetic dataset production is a strong fit especially for non-human, object, and industrial categories — products, parts, warehouse items, packaging, and similar subjects.
What kinds of projects are the best fit?
Best-fit projects include customer-provided image datasets that need ML-ready annotation, custom synthetic production for narrow CV use cases, and ongoing dataset ops for teams that need structured delivery without building internal tooling.
Need a custom dataset workflow for your ML team?
Tell us what you need and we'll see if there's a fit.