TimeSformer K400 is Meta's divided space-time attention model — recognizing 400 human actions in video using a pure-transformer architecture without 3D convolutions.
TimeSformer K400 is an AI model released 2021 by facebook, a leading organization in large language model research. Meta's TimeSformer fine-tuned on Kinetics-400 — recognises 400 human actions in video clips (running, cooking, sports, etc.). With ~121M and a a large context window context window, TimeSformer K400 is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.
TimeSformer K400 uses a dense transformer architecture with ~121M of parameters, all of which are active for every input token. Dense architectures provide predictable, consistent behavior across diverse task types — there are no "cold" experts to warm up, and quality is uniform across prompts. facebook's training pipeline combined large-scale pretraining on a large-scale multilingual pretraining dataset with supervised fine-tuning and alignment techniques (RLHF or DPO), producing TimeSformer K400's characteristic balance of capability, helpfulness, and safety.
As of 2026-10-16, TimeSformer K400 by facebook demonstrates measurable performance on standard AI evaluation benchmarks. On Kinetics-400 Top-1 — which measures model performance on specialized evaluation tasks — it achieves 77.9%. These scores place TimeSformer K400 — at ~121M of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
The primary strengths of TimeSformer K400 are: Space-time attention for video understanding — captures both spatial and temporal features; CC-BY-NC 4.0 license; Strong on Kinetics-400 video classification. These make it particularly well-suited for Video action recognition research, Sport and activity classification. Users working on complex, multi-step problems benefit most from TimeSformer K400's ability to maintain coherence across long inputs and produce structured, verifiable outputs.
Like all current AI systems, TimeSformer K400 has limitations: CC-BY-NC — not for commercial use; Classification only — no video generation. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. facebook continues to improve TimeSformer K400 through ongoing research.
TimeSformer K400 is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making TimeSformer K400 accessible to students, researchers, developers, and early-stage teams globally.
As of 2026-10-16, TimeSformer K400 by facebook demonstrates measurable performance on standard AI evaluation benchmarks. On Kinetics-400 Top-1 — which measures model performance on specialized evaluation tasks — it achieves 77.9%. These scores place TimeSformer K400 — at ~121M of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
TimeSformer: divided space-time attention for video understanding
The training pipeline for TimeSformer K400 involves multiple stages: large-scale pretraining on diverse multilingual text and code corpora to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. facebook's research team — based in United States — has optimized TimeSformer K400 for the specific capability profile described above.
Inference efficiency: TimeSformer K400 at ~121M is designed to deliver quality outputs at practical serving speeds. Its context capacity supports complex multi-turn conversations without quality degradation.
Getting started with TimeSformer K400 on LITHOVEX AI takes under two minutes. Follow these steps:
For best results with TimeSformer K400: provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions.
Sports performance analysis and coaching AI
Workplace safety monitoring for industrial settings
Physical rehabilitation exercise tracking systems
Fitness application automatic exercise recognition
Video surveillance activity detection
facebook is a leading organization in AI research and large language model development. Their team has built expertise across natural language processing, model architecture design, and alignment research. facebook's models are used by researchers, developers, and enterprises worldwide. Their commitment to advancing AI capability while maintaining responsible deployment practices has made them a respected contributor to the global AI ecosystem. The organization continues to publish research and release models that push the boundaries of what AI systems can achieve.
Founded: 2004, headquartered in United States.
TimeSformer K400 by facebook competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.
Both TimeSformer K400 and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.
TimeSformer K400 is released under CC BY-NC 4.0, which means you can download and run it on your own hardware. Below are the quickest ways to get started.
LITHOVEX AI runs TimeSformer K400 on cloud infrastructure, so you can use it instantly in your browser without downloading anything, configuring drivers, or managing VRAM. Create a free account and start in under 60 seconds.
Human action recognition has applications from sports analytics in North America to security monitoring in Asia to medical rehabilitation tracking in Europe. TimeSformer's transformer-based approach avoids the 3D convolution complexity of prior models, making it more broadly accessible.
TimeSformer K400 by facebook has the following version history and notable updates:
For the complete changelog and latest release notes, visit the official facebook model page.