Most computer vision projects don’t stall at the model. They stall at the data. Teams have the architecture, the compute and the engineers, and then spend months trying to source images and video that actually match the conditions their model will meet in production. That sourcing problem is what our computer vision data catalog exists to solve, and we have just made it considerably bigger. New image and video datasets have been added across every category in the catalog, from face biometrics and anti-spoofing through to OCR, fashion, industrial inspection and long-form video.
Why teams license visual data instead of collecting it
Building a dataset in-house is rarely a two-week job. Recruiting participants across the right demographics, running the capture, handling consent, then annotating to a usable standard can take a full quarter before a single training run happens. Licensing an existing dataset compresses that into a conversation and a contract. It also makes it cheaper to test a hypothesis. If a model needs a particular kind of data to work, it is far better to find that out with licensed data in week one than after six months of your own collection.
What’s new, category by category
The update touches all eight dataset categories in the catalog. Here is what stands out in each.
Anti-spoofing
This is now the largest category in the catalog, with 25 datasets mapped to iBeta certification levels. Level 1 coverage includes 35,800 videos, 22,000 paper mask attack videos and IR + RGB paper attacks for webcam onboarding. Level 2 adds a 45,522-video set, 25,000 3D mask attack videos, more than 11,000 cloth mask videos and 12,500 silicone mask attacks across 18 hyper-realistic masks. For Level 3, there are more than 10,000 premium resin mask attack videos. Replay coverage includes 5,000+ display replay videos and 3,000 mobile replay attacks across 15 devices.
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Facial and body part recognition
This is the deepest part of the refresh. New additions include a 10-million-image face recognition dataset, a 44,000-image Asian facial collection, a 70,000-image selfie-and-official-ID dataset covering more than 6,000 identities, and a 10,000-participant multi-distance smartphone facial dataset from India. Two historical datasets capture genuine ageing across six months to more than ten years using real photographs rather than synthetic ageing, which matters for age-invariant recognition and KYC. A kids age progression dataset and a synthetic children's faces dataset address a well-documented gap: children are largely absent from age detection and deepfake prevention data, and the synthetic set widens demographic coverage without using any real-person data.
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Language and text (OCR)
The newest additions lean towards Arabic and Chinese. They include 25,000 Arabic calligraphy images gathered from public archives and working calligraphers, 15,000 handwritten and scanned Arabic documents, 10,000 street and storefront signs containing Arabic text, and a Chinese publication library spanning books, journals and public policy material. Useful for scene text detection, handwriting recognition and document digitisation in scripts that most open datasets underserve.
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Clothing and fashion
Retail and fashion models can draw on a 50,000-image RAW photography dataset spanning celebrity and studio headshots, fashion and studio series, sport and active wear, and product and lifestyle shoots.
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Gesture, pose and activity
Two datasets lead this category: 500 hours of Arabic Sign Language video across multiple dialects for sign language recognition and translation, and a 24,000-image smartphone palm recognition set captured from 2,000 participants worldwide.
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Machine and industry
Insurance, traffic and mobility use cases are well served: 69,983 damaged vehicle images from the UK, licence plate and damaged car images from Europe, automotive video of cars from Europe, and a CCTV collection from South Africa covering vehicle footage, licence plate images and 56 hours of accident footage for incident and anomaly detection.
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Object and contour segmentation
Four datasets stand out: a 15,000-image food, recipe and cooking dataset created by chefs for multimodal and LLM training, a grocery product collection for shelf monitoring, SKU recognition and automated checkout, 200,000 interior and exterior house photos from the USA, and a real-world object and property collection of more than 500 million images spanning real estate, vehicles, consumer electronics and collectibles.
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Video at scale
For teams training video and multimodal models, the 'other datasets' category now carries serious volume: 80,000 hours of YouTube video, 10,000 hours of AI interview videos, 3,000 hours from a documentary filmmaker collection spanning eight countries, 1,000 hours of AAA gameplay footage, 1,000 hours of martial arts fights, a 1.2-million-minute licensed news, weather, sports and events library, and 40,000+ stock clips covering human activity, emergencies and wildlife.
Know moreHow these Computer Vision datasets are built
Every dataset in the catalog is ethically sourced with contributor consent and handled in line with GDPR and other global privacy standards, with de-identification available where a use case calls for it. Datasets are delivered in standard formats such as JSON, CSV or XML with metadata attached, so they drop into existing training pipelines without a conversion step. Where a dataset ships unannotated, our annotation teams can label it to your specification. And where the catalog doesn’t hold exactly what you need, we can collect it.
Where to start
Browse the catalog by category and request a sample of anything that looks relevant. If your requirement is specific, whether that’s a demographic split, a lighting condition or a particular object class, tell us and we will either point you to the closest fit or scope a custom collection. New datasets are being added continuously, so it is worth checking back.
