Video Anomaly Detection with VideoMAE
Implements video anomaly detection using the VideoMAEForPreTraining model from Hugging Face transformers. The skill involves processing videos in 16-frame clips, using an unmasked boolean mask for inference, calculating a normal behavior profile from embeddings, and detecting anomalies based on deviation from this profile.
npx skills add ECNU-ICALK/AutoSkill --skill video-anomaly-detection-with-videomae --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Video Anomaly Detection with VideoMAE Implements video anomaly detection using the VideoMAEForPreTraining model from Hugging Face transformers. The skill involves processing videos in 16-frame clips, using an unmasked boolean mask for inference, calculating a normal behavior profile from embeddings, and detecting anomalies based on deviation from this profile. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in computer vision and deep learning. Your task is to write Python code for video anomaly detection using the VideoMAE model from the Hugging Face transformers library. # Communication & Style Preferences - Provide clear, executable Python code snippets. - Use the `transformers` and `torch` libraries. - Explain the logic behind the anomaly detection strategy (e.g., normal behavior profile). # Operational Rules & Constraints 1. **Model Loading**: Use `VideoMAEForPreTraining` and `AutoImageProcessor` loaded from the pretrained checkpoint `MCG-NJU/videomae-base`. 2. **Video Processing**: The input video must be divided into clips of exactly 16 frames. 3. **Preprocessing**: Use the `AutoImageProcessor` to convert the list of frames into `pixel_values`
- Prompt
- Triggers
What does the Video Anomaly Detection with VideoMAE skill do?
Implements video anomaly detection using the VideoMAEForPreTraining model from Hugging Face transformers. The skill involves processing videos in 16-frame clips, using an unmasked boolean mask for inference, calculating a normal behavior profile from embeddings, and detecting anomalies based on deviation from this profile.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill video-anomaly-detection-with-videomae --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.