Built for AI that meets the physical world.
Every industry has its own long tail. Below: the common data problems, example datasets, annotation needs, edge cases, and recommended Datafy Lab services for each.
Warehouse Robotics
Picking, sorting, barcode handling, damaged packages, cluttered bins, conveyor workflows, failed grasps, and unusual object positions.
Common data problems
- Demo-only performance
- Cluttered, occluded bins
- Reflective / damaged labels
Example datasets
- Pick-and-place sequences
- Bin clutter imagery
- Conveyor workflow video
Annotation needs
- Grasp point keypoints
- Object segmentation
- Failure reason tagging
Edge cases
- Failed grasps
- Damaged packages
- Unusual object angles
Recommended Datafy Lab services
Edge-Case Dataset Creation →Multimodal Annotation →Continuous Data Foundry →Humanoid Robotics
Egocentric task video, human demonstrations, tool usage, manipulation sequences, household-like tasks, and workplace task understanding.
Common data problems
- Scarce human POV data
- Long-horizon task gaps
- Manipulation generalization
Example datasets
- Egocentric task videos
- Hand-object interactions
- Step-by-step demonstrations
Annotation needs
- Action segmentation
- Hand-object interaction labels
- Task completion markers
Edge cases
- Rare tool usage
- Failure recoveries
- Cluttered home/work scenes
Recommended Datafy Lab services
Egocentric Video Data →Human Task Demonstration →Multimodal Annotation →Manufacturing Inspection
Rare defects, surface anomalies, lighting variation, packaging issues, assembly errors, and quality-control edge cases.
Common data problems
- Defect-class imbalance
- Lighting variation
- Few examples of rare faults
Example datasets
- Surface defect imagery
- Assembly error footage
- Packaging anomaly sets
Annotation needs
- Defect labels
- Segmentation masks
- Severity classification
Edge cases
- Rare defects
- Reflective surfaces
- Subtle assembly errors
Recommended Datafy Lab services
Edge-Case Dataset Creation →Dataset Certification →Data Failure Audit →Autonomous Inspection
Drones, mobile robots, infrastructure inspection, utilities, construction sites, industrial facilities, and hazardous environments.
Common data problems
- Hard-to-access sites
- Weather & terrain variation
- Safety-critical rare events
Example datasets
- Infrastructure imagery
- Facility inspection video
- Hazard scenario capture
Annotation needs
- Object detection
- Anomaly labels
- Scene metadata
Edge cases
- Hazardous conditions
- Occlusion & glare
- Unusual structures
Recommended Datafy Lab services
Field Data Capture →Edge-Case Dataset Creation →Multimodal Annotation →Retail Vision AI
Shelf monitoring, checkout vision, inventory detection, crowding, occlusion, object variation, and in-store visual intelligence.
Common data problems
- Crowding & occlusion
- High SKU variation
- Checkout edge cases
Example datasets
- Shelf monitoring imagery
- Checkout video
- Inventory detection sets
Annotation needs
- Object detection
- Fine-grained classification
- Tracking through occlusion
Edge cases
- Crowded scenes
- Look-alike SKUs
- Partial occlusion
Recommended Datafy Lab services
Multimodal Annotation →Edge-Case Dataset Creation →Dataset Certification →Agriculture Robotics
Crop detection, weeds, disease, harvesting conditions, terrain complexity, weather variation, and fruit quality datasets.
Common data problems
- Seasonal variation
- Terrain complexity
- Disease class scarcity
Example datasets
- Crop & weed imagery
- Disease progression sets
- Harvest condition video
Annotation needs
- Segmentation masks
- Disease classification
- Ripeness/quality labels
Edge cases
- Weather variation
- Occluded fruit
- Rare disease states