Workload fit
google/vivit-b-16x2-kinetics400 is categorized for Video classification. Its current record accepts video and produces label. Confirm file formats, preprocessing, and provider-specific request schemas before implementation.
google/vivit-b-16x2-kinetics400 is a source-linked Hugging Face repository assigned to Video classification. Its recorded task interface accepts video and produces label.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
google/vivit-b-16x2-kinetics400 is a Hugging Face repository returned by the public Video classification task feed. Its recorded download count is 43,992 and it has 41 likes at the time this draft was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Video classification. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists transformers as its library metadata. Its recorded tags include transformers, pytorch, vivit, vision, video-classification, arxiv:2103.15691, license:mit, endpoints_compatible. These source details are retained so an editor can trace this directory entry back to the repository instead of relying on copied descriptions.
Before approval, verify the model card, license, training data disclosures, hardware requirements, supported languages, intended use, known limitations, and any dependency or safety requirements. Test the exact workload: inputs, output format, latency, memory use, evaluation metric, and deployment environment. Do not treat downloads or likes as a substitute for measured task quality.
This is a source-backed draft. SyncDev does not infer an API offering, price, benchmark score, context limit, or commercial availability from a Hugging Face repository alone. A human editor must verify material claims, add authoritative evidence, and approve the record before it can become public or enter the sitemap.
The Hugging Face task assignment records video as input and label as output for google/vivit-b-16x2-kinetics400. This interface summary comes from the repository’s recorded Video classification task classification. It helps readers distinguish a text, image, audio, video, document, embedding, or prediction workflow without inferring undocumented API behavior. Model wrappers can expose different preprocessing and response formats, so verify the upstream model card and the exact runtime before deployment.
Use the recorded facts as a shortlist, then validate the model against representative inputs and production constraints.
google/vivit-b-16x2-kinetics400 is categorized for Video classification. Its current record accepts video and produces label. Confirm file formats, preprocessing, and provider-specific request schemas before implementation.
The directory records — context and — maximum output. Listed token prices are — input and — output per 1M tokens. Treat missing values as unknown and recheck current commercial terms.
Recorded output speed is — and time to first token is —. Hosting route, region, prompt length, concurrency, and provider load can materially change both measurements.
This page separates sourced model facts from incomplete fields. Benchmark evidence is displayed only when its source is linked and verified. Before choosing google/vivit-b-16x2-kinetics400, test task quality, tool reliability, safety behavior, data controls, rate limits, and total cost on the exact route you intend to use.
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