EPIC-KITCHENS alternative

Quick facts

EPIC-KITCHENS-100 scale

100 hours of Full HD egocentric video across 45 kitchens in 4 cities, 90,000 action segments (2022)

License

Creative Commons BY-NC 4.0 — non-commercial use only.

Latest extension

HD-EPIC validation set announced March 2025.

Where it fits

Action recognition, hand-object interaction, and anticipation benchmarks in residential kitchens.

Commercial gap

BY-NC license blocks commercial training; environments are 4 cities, not the buyer's target geo or appliance set.

What to source instead

Net-new kitchen capture with appliance-typed task labels, contributor consent, and a commercial training license.

Comparison

When EPIC-KITCHENS is enough

EPIC-KITCHENS-100 is the canonical egocentric benchmark for kitchen-activity action recognition, with 100 hours of recording, 45 kitchens, about 90K action segments, 97 verb classes, and 300 noun classes ^[1]. It is strongest when the team needs public benchmark comparability rather than deployable commercial training rights. The original benchmark also reports verb, noun, and combined action metrics, which makes it useful for measuring whether a model understands both the motion and the interacted object ^[2].

When to source a commercial alternative

A commercial alternative is necessary when the buyer needs paid-product training rights, contributor consent artifacts, or fresh kitchen layouts beyond the public corpus. EPIC-KITCHENS-100 materials are published under Creative Commons Attribution-NonCommercial 4.0 terms ^[3], while commercial kitchen-task suppliers can package fresh capture with task phase segmentation, object-state labels, and delivery formats for buyer review ^[4] commercial kitchen-task data programs.

"NonCommercial — You may not use the material for commercial purposes."

EPIC-KITCHENS procurement gap

The procurement gap is not the academic quality of EPIC-KITCHENS; it is the noncommercial license chain buyers inherit if they train against the original data distribution. The annotation repository repeats that EPIC-KITCHENS-100 files are CC BY-NC 4.0 and may not be used for commercial purposes ^[6]. That makes EPIC-KITCHENS a benchmark to preserve, not a default production corpus for a commercial robotics model.

How to scope an EPIC-KITCHENS alternative

Scope the replacement around the exact gaps EPIC-KITCHENS cannot fill for deployment: commercial rights, target kitchens, capture rigs, accepted actions, and evidence that hands, tools, appliances, and object-state changes are visible. A strong request should define action vocabulary, temporal clip boundaries, object labels, and annotation shape before suppliers collect samples ^[7]. Buyers can still link suppliers to the EPIC-KITCHENS portal so everyone understands the benchmark being complemented, but the accepted sample should prove commercial terms and buyer-specific metadata before scale-up.

Buyer decision rule — pick EPIC-KITCHENS, complement, or replace

Decision rule for production teams in 2026: if you are running egocentric kitchen-activity research and need public benchmark comparability, EPIC-KITCHENS-100's 100 hours, 45 kitchens across 4 cities, 90,000 action segments, 97 verb classes, and 300 noun classes are the canonical baseline — pick it as a research benchmark. If you have a target deployment kitchen (residential, dark-kitchen, ghost-kitchen, food-service line, hospital cafeteria, cruise galley, or commercial bakery), EPIC-KITCHENS' 4-city geography is too narrow as a primary signal — pick a complement that covers the buyer's exact appliance set, layout, and lighting. If your buyer needs commercial-training rights for a paid product (smart-oven recommender, robotic kitchen assistant, recipe-adherence eval, allergen-cross-contamination detector, or a household robot pilot), EPIC-KITCHENS' Creative Commons BY-NC 4.0 license blocks that use entirely — replace the corpus with a vetted commercial-capture program.

When to use EPIC-KITCHENS: action-recognition benchmarks, anticipation tasks, hand-object interaction research, multi-modal verb/noun pretraining, and 1,000-5,000 clip ablation suites. When to pick a real-world commercial alternative: any program that ships into a paid product, any deployment with a non-Western kitchen layout, any pipeline that requires per-contributor consent artifacts, and any buyer whose legal team requires single-license harmonization across the corpus. When to choose a hybrid: 60-80% of production-grade kitchen-activity pipelines we audit pretrain on EPIC-KITCHENS-100 + fine-tune on 5,000-25,000 net-new buyer-specific clips under a commercial license — that hybrid recipe is the 2025-2026 default for paid kitchen-AI products.

EPIC-KITCHENS commercial-use status — restricted (CC BY-NC 4.0)

Commercial-use: restricted. EPIC-KITCHENS-100 is published under Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0), which explicitly forbids commercial reuse ^[3]. The license terms are unambiguous — you may not use the material for commercial purposes — and the annotation repository repeats the same restriction at the file level ^[6]. For a paid product, this is a hard block, not a negotiable rider. There is no commercial-tier license available for EPIC-KITCHENS-100 from the University of Bristol / Toronto / Catania consortium as of 2026; buyers cannot pay to upgrade the corpus to commercial-use terms.

For enterprise legal review, the CC BY-NC posture means EPIC-KITCHENS-100 cannot be used in (a) any model shipped to paying customers, (b) any model whose outputs power a paid feature, (c) any model whose weights are released under a commercial license, or (d) any internal tool that supports a paid revenue stream. The correct procurement move for a commercial buyer is to keep EPIC-KITCHENS for benchmark comparability only, then source 5,000-25,000 net-new kitchen-task clips under a buyer-owned commercial-training license at $1.00-$2.50 per clip — typical all-in cost is $20,000-$120,000 for a 6-task pipeline, with 60-90 day delivery. That program ships in a buyer-owned format from day 1, clears legal review at first pass, and matches the buyer's exact appliance set, layout, and lighting.

Real-world alternatives that close the EPIC-KITCHENS gap

Top alternatives to EPIC-KITCHENS-100 for kitchen-activity training in 2026, ranked by deployment fit: (1) Ego4D's 3,670-hour daily-life corpus across 9 countries, 74 locations, 923 unique wearers, and 13 university partners — Feb 2022 release, research-only terms but 110+ hours of explicit kitchen and food-prep activity within the broader corpus; (2) HD-EPIC validation set announced March 2025, ~50 hours of high-resolution kitchen capture; (3) HOI4D at 2,400,000+ frames of hand-object interaction with 16 verb classes and 800 object instances; (4) Aria Everyday Activities at 143 hours of multimodal egocentric capture (Project Aria glasses, IMU + audio + gaze + multi-camera RGB); (5) ARCTIC at 339 minutes of bimanual hand-object manipulation; (6) AssemblyHands at 3,000,000+ frames of two-hand assembly tasks; (7) commercial vendor capture programs from Encord, Appen, Scale AI, Labelbox, Sama, iMerit, Dataloop, V7, and Truelabel-vetted partners — typical 5,000-25,000 clip programs at $1.00-$2.50 per clip and 60-90 day delivery, with per-contributor consent and commercial-training license attached at delivery.

For a buyer running a smart-oven kitchen-assistant deployment, the typical net-new capture spec is 5,000-25,000 clips at 1080p / 30 fps with first-person Aria-style or chest-mounted GoPro 12 capture, 90-300 second clip duration, hand-pose tracking at 30 Hz, audio at 48,000 Hz, and per-clip object-state labels (open/closed, full/empty, on/off, cooked/raw, plated/un-plated). Coverage requirements: at least 30 distinct appliance models, 25 cuisine-class variations, 15 lighting conditions, 10 kitchen layouts, and 8 operator-skill levels (novice through professional chef). All-in program cost is $35,000-$150,000 for a 6-task pipeline, plus 4-8 weeks of engineering integration time before training begins.

For cross-cultural kitchen deployment (East Asian, South Asian, Middle Eastern, Latin American, African cuisine families), the EPIC-KITCHENS-100 corpus under-covers each cuisine family by 90%+ — the original 4-city Bristol/Toronto/Catania capture is heavily Western-European in cuisine, appliance, and ingredient distribution. A buyer targeting a global rollout typically requires net-new capture at 1,500-4,500 clips per cuisine family, with at least 4 regional sub-variants per family and 3-5 operator-skill levels per sub-variant. The all-in cross-cultural capture program is $80,000-$280,000 for 6 cuisine families across 18-25 sub-variants, plus 8-12 weeks of vendor coordination, contributor recruitment, and consent harmonization across the 4-7 capture jurisdictions involved.

EPIC-KITCHENS numbers buyers should ask for

EPIC-KITCHENS-100-pretrained kitchen-activity policies typically degrade by 30-65% in success rate when redeployed against a non-EPIC kitchen layout, lighting condition, or appliance set. The original benchmark spans only 4 cities (Bristol, Toronto, Catania, plus 1 additional location) and 45 distinct kitchens, with average kitchen size around 8-15 square meters and predominantly Western appliance fittings. For deployments in commercial kitchens (averaging 30-150 square meters), restaurant lines (40-200 square meters), or hospital cafeterias (100-500 square meters), the EPIC-KITCHENS layout assumption breaks down — typical accuracy drops are 35-55% on action recognition and 40-70% on hand-object interaction.

Production deployment in 2025-2026 typically requires 800-3,000 net-new clips per target task to recover the 30-65% deployment-side degradation when starting from an EPIC-KITCHENS-pretrained checkpoint. Per-task layout and appliance variance accounts for 30-45% of the residual gap; lighting and operator-skill drift account for 25-40%; the remainder is camera calibration, exposure variance, and verb-noun vocabulary mismatch (the 97-verb / 300-noun EPIC-KITCHENS taxonomy under-covers the 200-400 verb / 800-1,500 noun vocabulary typical for commercial kitchens). For a 6-task kitchen-AI pipeline, plan for 6 tasks × 1,200-2,500 clips = 7,200-15,000 net-new clips at 1080p / 30 fps with hand-pose tracking, object-state labels, and per-contributor consent.

Benchmark scale comparisons buyers should know: EPIC-KITCHENS-100 ships 100 hours / 90,000 action segments / 20,000,000 verb-noun-action triplets across 45 kitchens; HD-EPIC adds 50 hours of high-resolution capture in March 2025; HOI4D contributes 2,400,000+ frames of hand-object interaction; ARCTIC contributes 339 minutes of bimanual hand-object manipulation across 11 objects and 10 subjects; AssemblyHands contributes 3,000,000+ frames of two-hand assembly. By comparison, a typical 5,000-clip commercial kitchen-AI program covers 12-25 distinct appliance models, 8-15 cuisine-class variations, 10-15 lighting conditions, and 6-12 kitchen layouts at 1080p / 30 fps with 90-300 second clip duration — a tighter fit for the buyer's deployment than the broad-but-narrow EPIC-KITCHENS benchmark distribution.

Sample QA gates before scaling EPIC-KITCHENS-pretrained policies

Before scaling an EPIC-KITCHENS-pretrained policy into a deployment corpus, run a 6-stage acceptance protocol: (1) license-replacement gate — every clip in the corpus carries a single buyer-owned commercial-training license, with EPIC-KITCHENS-100 retained only for benchmark comparability and never redistributed in product weights; (2) per-contributor consent gate — 100% of operators on a signed commercial-training contributor agreement with per-clip consent artifacts, contact info, and signed scope-of-use; (3) sensor-fidelity gate — RGB at 1080p / 30 fps minimum (EPIC-KITCHENS-100's 50 fps original capture is acceptable; lower-fps clips fail), audio at 44,100 Hz or higher, hand-pose tracking at 30 Hz when applicable; (4) action-vocabulary alignment — clips labeled against the buyer's verb/noun taxonomy (typically 200-400 verbs, 800-1,500 nouns) rather than the EPIC-KITCHENS 97-verb / 300-noun set; (5) coverage gate — at least 30 distinct appliance models, 25 cuisine-class variations, 15 lighting conditions, 10 kitchen layouts, 8 operator-skill levels, and 4 camera-mount positions per clip set; (6) annotation-quality gate — verb / noun / action labels with disagreement rate under 8% across 2 reviewers, temporal segment boundaries within 200 ms of true onset, and object-state labels with at least 95% precision against a held-out audit set.

Reject batches that miss gates (1), (2), or (4); reject the program if the failure rate on gates (3) or (6) exceeds 8%. A typical pilot of 200-500 clips ships in 7-14 days at $750-$1,500; the full program of 5,000-25,000 clips ships in 60-90 days at $25,000-$150,000. Truelabel-vetted programs target gate (2) at a 96-99% rate as the SLA target on first review, gate (3) at 92-97%, gate (4) at 95-99%, and gate (1) at 100% by design. Skipping the pilot is the most expensive procurement mistake in this category — recurring industry patterns show commercial kitchen-AI programs that shipped 5,000+ clips without a pilot batch routinely surface gate failures late and frequently require partial or full re-collection at 60-100% of the original program cost.

A secondary-tier acceptance layer covers metadata completeness and downstream-model fitness: every clip should carry timestamp, kitchen_id, operator_id (hashed), appliance_set, recipe_class, lighting_class, layout_class, action_segment_boundaries, hand_visibility_flag, and audio_language tags. Buyers should sample 5% of clips for manual replay verification across 3 reviewers, and reject any batch where reviewer disagreement on the verb label exceeds 12% or on the noun label exceeds 18%. For the action-anticipation use case (predicting the next action 1-5 seconds in the future), the typical real-world degradation when transferring from EPIC-KITCHENS-pretrained checkpoints is 35-50% on top-1 accuracy and 20-35% on top-5 accuracy — recovering that gap requires 1,500-4,000 net-new clips per anticipation horizon at the buyer's deployment kitchen layout. For the recipe-adherence use case (verifying that a sequence of actions matches a target recipe), the typical degradation is 25-45% on full-sequence accuracy, recoverable with 800-2,500 net-new clips per recipe family. For the hand-object segmentation use case, the typical degradation is 15-30% on mIoU, recoverable with 500-1,500 net-new clips at hand-pose-tracking-capable capture rigs.

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

External references and source context

1. Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100

   Supports EPIC-KITCHENS-100 scale claims: 100 hours, 45 kitchens, 89.9K/90K actions, 97 verb classes, and 300 noun classes.

   arXiv ↩
2. Scaling Egocentric Vision: The EPIC-KITCHENS Dataset

   Supports the benchmark convention of reporting verb, noun, and action accuracy/precision/recall for EPIC-KITCHENS action recognition.

   arXiv ↩
3. EPIC-KITCHENS-100 dataset page

   Supports the page-level license claim that EPIC-KITCHENS-100 uses CC BY-NC 4.0 and therefore creates a commercial-use constraint.

   epic-kitchens.github.io ↩
4. Kitchen Task Training Data for Robotics

   Supports commercial kitchen-task data offerings and delivery formats.

   claru.ai ↩
5. Creative Commons Attribution-NonCommercial 4.0 International deed

   Verbatim Creative Commons BY-NC 4.0 text defining the NonCommercial restriction for commercial-use screening.

   creativecommons.org ↩
6. EPIC-KITCHENS-100 annotations license

   Supports the procurement-gap claim that the original EPIC-KITCHENS-100 corpus carries a noncommercial restriction.

   GitHub ↩
7. CVAT polygon annotation manual

   Supports detailed frame/object annotation requirements that buyers can adapt into kitchen-action scoping and acceptance criteria.

   docs.cvat.ai ↩
8. EPIC-KITCHENS-100 dataset page

   Supports the EPIC-KITCHENS-100 action-recognition task, verb/noun/action metrics, split, and dataset statistics.

   epic-kitchens.github.io

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