video benchmark
ActivityNet
A large-scale video benchmark for human activity understanding. Provides samples from 203 activity classes with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 video hours. The benchmark covers a wide range of complex human activities that are of interest to people in their daily living and can be used to compare algorithms for three scenarios: untrimmed video classification, trimmed activity classification, and activity detection.
Updated Aug 11, 2026
Models1
Model coverage1
MetricScore
EvidenceB
ActivityNet Ranking
Higher score ranks better on this benchmark.
| 01 | OP | 61.9% | 100.0% | 1 | C |
ActivityNet Highlights
The leading models and scores on this benchmark.
What is ActivityNet?
What ActivityNet measures and how its scores work.
A large-scale video benchmark for human activity understanding. Provides samples from 203 activity classes with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 video hours. The benchmark covers a wide range of complex human activities that are of interest to people in their daily living and can be used to compare algorithms for three scenarios: untrimmed video classification, trimmed activity classification, and activity detection.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- ActivityNet
- Modality
- video
- Primary category
- video
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- activitynet|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about ActivityNet.
Which model scores highest on ActivityNet?
GPT-4o is currently ranked first with 61.9%.
What does ActivityNet measure?
A large-scale video benchmark for human activity understanding. Provides samples from 203 activity classes with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 video hours. The benchmark covers a wide range of complex human activities that are of interest to people in their daily living and can be used to compare algorithms for three scenarios: untrimmed video classification, trimmed activity classification, and activity detection.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.