Loose or missing boxes
Poor boxes teach the model to detect badly.
Image labeling and classification for computer vision
Consistent, reviewed image labels for training and testing vision models, from simple classification to detailed bounding boxes and segmentation.
Quoted per project · How pricing works
What it is
Image labeling, explained
Vision models are sensitive to label noise: a missed object, a loose box or an inconsistent class boundary teaches the wrong thing. TestLancer annotators follow your guidelines with visual examples, and every batch is sampled by reviewers before delivery, so you can train with confidence.
Who it's for
- Retail and product recognition projects
- Agriculture and environment imaging
- Document and receipt processing
- Security and safety detection
- Robotics and autonomous systems research
- Startups building a first vision dataset
Problems solved
What this fixes
Ambiguous classes
Edge cases labeled differently by different people.
Slow in-house labeling
Engineers spend their time labeling instead of building.
Unmeasured quality
No numbers on how accurate your dataset really is.
What's included
What you get with Image labeling
Image classification
Single or multi-label categories.
Bounding boxes
Object detection labels with tight boxes.
Polygons and segmentation
Precise outlines for irregular objects.
Keypoints
Landmarks for pose and shape tasks.
Attribute tagging
Colour, condition, orientation and other properties.
Review and QA
Sampling, corrections and accuracy reporting.
How it works
From brief to results
- Share the projectData type, volume, labels and quality target.
- Pilot batchA small batch to agree guidelines and accuracy.
- Workforce assignedTrained members matched to your language and domain.
- Production with QAWork in batches with review and consistency checks.
- Deliver and approveData in your format, with quality metrics.
Deliverables
- Annotations in COCO, YOLO, Pascal VOC, CSV or custom format
- Per-batch accuracy results
- Log of ambiguous images and decisions
- Updated guideline document
- Final delivery summary
Image labeling: frequently asked questions
How accurate are the labels?
Accuracy is agreed in the pilot and measured on every batch through review sampling and gold-standard images.
Can you work with our own tool?
Yes, if annotators can be given access. Otherwise we use a suitable tool and export to your format.
How many images can you handle?
From a few hundred for a pilot to large recurring volumes. Throughput depends on label complexity; we'll estimate it after the pilot.
Do you label faces or personal data?
Only with a clear lawful basis and your data protection requirements agreed in advance.
For businesses & agencies
Start your project with real people.
Testing, research, AI data or skilled freelance work. Tell us what you need and get a clear scope and price before anything starts.
For testers & freelancers
Turn your skills into opportunities.
Testing, research, AI tasks and freelance work. One free account. Rewards for approved, genuine work.
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