Human Action Classification
MC3-18 fine-tuned on UCF-101 for clip-level human action recognition.
A reliable, lightweight baseline for UCF-101 action recognition that beats the original paper's accuracy. Best suited for researchers and engineers who need a quick, reproducible starting point for short-video classification without heavy compute. If you need real-time single-frame classification or untrimmed video detection, look at alternatives like I3D or R(2+1)D.
- Video understanding researchers needing a quick UCF-101 baseline
- Computer vision engineers building action recognition pipelines on a budget
- Prototyping low-compute video classification systems
- Educators teaching video analysis with deep learning
- Real-time single-frame classification (requires 16-frame clips)
- Long-horizon temporal reasoning (trained on short trimmed clips)
- Untrimmed video detection without adaptation
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Skip Human Action Classification if you need real-time single-frame classification, untrimmed video detection, or recognition beyond the 101 UCF-101 action classes.
Because the model is free and open-source, there are no hidden costs for the model itself; however, compute costs for inference (GPU/CPU) are your responsibility.
The model is free and open-source under Apache-2.0, suitable for any budget. Unlike hosted APIs that charge per inference, you only pay for your own compute. This makes it ideal for researchers and hobbyists.
In short
Human Action Classification — MC3-18 fine-tuned on UCF-101 for clip-level human action recognition. Best for Video understanding researchers needing a quick UCF-101 baseline, Computer vision engineers building action recognition pipelines on a budget, Prototyping low-compute video classification systems. Free to use.
What's new in Human Action Classification
Checked 12 days agoAcross the latest 10 updates: 8 feature updates and 2 news mentions.
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Viability Score
How likely is Human Action Classification to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Fine-tuned on UCF-101 for 101 action classes
- MC3-18 architecture with mixed 2D/3D convolutions
- Pretrained on Kinetics-400
- 87.05% accuracy on UCF-101 Split 1 (test set)
- Lightweight ~11.7M parameters
- Input: 16-frame clips at 112x112 resolution
- Output: clip-level action class logits
- Apache-2.0 license
- Supports PyTorch inference
- Available via Hugging Face Hub
- Self-reported F1 score 0.857, Precision 0.868
- Trained for 200 epochs with SGD optimizer
- ColorJitter, RandomHorizontalFlip, RandomCrop augmentation
About Human Action Classification
This model is an MC3-18 (Mixed 3D Convolutions) network fine-tuned on the UCF-101 dataset for human action recognition. The architecture combines 2D and 3D convolutions, delivering an efficient temporal-spatial representation while maintaining a lightweight parameter count of approximately 11.7M. It is intended for researchers and engineers working on video-understanding pipelines. The primary use case is action classification in short, trimmed videos similar in distribution to UCF-101. It can also serve as a baseline for low-compute video applications. To use the model, load it from the Hugging Face Hub with PyTorch, apply the specified transforms (resize to 128x171, center crop to 112x112, normalize), and pass a 16-frame video tensor of shape CxTxHxW. Inference yields logits over 101 action classes. The model achieves 87.05% accuracy on UCF-101 Split 1, outperforming the published baseline by 2.05 percentage points. It is available under the Apache-2.0 license. For research purposes, this model offers a solid starting point for clip-level classification without requiring heavy computational resources. Alternatives like I3D or R(2+1)D may offer higher accuracy on complex datasets but at a higher parameter count and inference cost.
Behind the Verdict
The MC3-18 model fine-tuned on UCF-101 is a solid, lightweight choice for clip-level action recognition. Its architecture blends 2D and 3D convolutions, keeping the parameter count low (~11.7M) while achieving 87.05% accuracy on UCF-101 Split 1. This is a 2.05% improvement over the published baseline, making it a dependable benchmark. The model is free under Apache-2.0, integrates via Hugging Face Hub, and is straightforward to use with PyTorch. Its main limitation is that it requires 16-frame clips, so it's not suitable for real-time single-frame tasks. It's designed for short trimmed videos, not untrimmed long-form video or action detection. For these use cases, you'd need to adapt models like I3D or R(2+1)D. The model is best for researchers, educators, and engineers prototyping low-compute video systems. It's not for production streaming inference or classes beyond the 101 UCF-101 categories. Overall, it's a practical tool for its niche.
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Real-world workflow fit
Concrete scenarios for the personas Human Action Classification actually fits — and what changes day-one when you adopt it.
You download the model from Hugging Face Hub and run inference on the UCF-101 test set to compare accuracy with your own architecture.
Outcome: You get a reproducible baseline (87.05% accuracy) within minutes, saving days of training from scratch.
You integrate the model into a PyTorch pipeline that processes short video clips and outputs action labels.
Outcome: Each 16-frame clip is classified into one of 101 actions, enabling automated tagging of sports or activity footage.
Use Cases
- Classify human actions in short video clips for sports analysis.
- Baseline for comparing new action recognition architectures on UCF-101.
- Tag video segments with activity labels in automated video annotation pipelines.
- Prototype a low-compute action recognition system for embedded devices.
Models Under the Hood
as of 2026-07-06
Limitations
- Trained only on UCF-101 (limited to 101 action classes).
- Requires 16-frame clips, making it unsuitable for real-time single-frame applications.
- Performance degrades on actions not represented in the training set.
as of 2026-07-06
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Human Action Classification tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Model
$0
Ideal for
Researchers, students, and engineers who need a free, open-source action recognition model for experimentation or integration.
What this tier adds
Starting tier: no cost, full model weights and code under Apache-2.0 license.
Where the pricing makes sense
The company stage and team size where Human Action Classification's pricing actually pencils out — and where peers do it cheaper.
The model is free and open-source under Apache-2.0, suitable for any budget. Unlike hosted APIs that charge per inference, you only pay for your own compute. This makes it ideal for researchers and hobbyists.
Setup time & first value
How long it actually takes to get something useful out of Human Action Classification — broken out by persona, not the marketing-page minute.
Researchers: under 30 minutes to download the model, set up transforms, and run inference with PyTorch. Engineers: integrate into existing PyTorch pipelines within a day.
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