Your label should match your deployment scenario
“Locomotion” sounds generic, but models learn different failure modes when you mix walking, jogging, and running versus training on a single gait. If your robot or app must handle only one action, a focused pack can train faster and generalize better than an everything-bucket—unless your roadmap truly requires mixed gaits.
Walking-heavy data
Walking datasets are ideal for surveillance analytics, retail gait cues, rehabilitation-style monitoring, and baseline human tracking. They usually emphasize stable poses and longer sequences with fewer extreme limb velocities.
Running-focused data
Running exports stress higher limb speed, different ground-contact patterns, and more aggressive motion blur risk. If your product is sports analytics or fast locomotion, prioritize datasets that report acceptance rates and visibility statistics for high-motion frames.
Full locomotion bundles
When you need a single training corpus that spans multiple gaits—walking, jogging, and running—a combined bundle reduces vendor fragmentation and keeps schema consistent across actions. Quality Vision publishes a full locomotion ready-made export with unified manifests and per-video splits; see the product page for frame counts and bundle details: Full Locomotion Pose Dataset (190 videos).
How to choose without overbuying
- Match your sensors: indoor vs outdoor lighting assumptions matter for vision features.
- Check augmentations: flips and noise can inflate row counts—use authentic pre-augmentation counts for fair comparisons.
- Validate early: parse a public sample and confirm your training code paths before purchasing a large bundle.
Next step: compare bundles and pricing
When you are ready to align budget with scope, compare ready-made options side-by-side on dataset pricing. You will find Gumroad checkout links for specific bundles and notes on export format (JSONL + metadata), so you can buy what fits your schedule—not a one-size-fits-all guess.
Dataset pricing (direct link)