Create README.md

README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+tags:
+- Video
+---
+
+# Atypical Dataset
+
+A curated video dataset capturing atypical, anomalous, and visually unconventional human activities. Sourced from real-world, synthetic, and artistic domains, it enables research into out-of-distribution video understanding.
+
+## Overview
+
+The **Atypical** video dataset introduces a diverse collection of short video clips that significantly diverge from everyday behavior and appearance. Unlike standard datasets that focus on typical human actions and natural scenes, **Atypical** emphasizes the rare, the surreal, and the unexpected—drawing from both real-life occurrences and fictional or synthetic creations.
+
+## Key Features
+
+- **Behavioral Anomalies:** Includes clips of unintentional behavior, accidents, and social norm violations.
+- **Visual Deviation:** Features surreal synthetic renderings, sci-fi environments, and staged theatrical content.
+- **Genre Diversity:** Integrates real-world, cinematic, animated, and artificial domains.
+- **Fine-grained Segmentation:** Clips are manually segmented into 2–10 seconds to ensure diversity and focus.
+- **Multi-source Compilation:** Videos are sampled from multiple publicly available datasets and online sources, curated with consistent formatting and labeling.
+
+## Data Composition
+
+### Unintentional & Abnormal
+
+- **Sources:**  
+  - [Oops Dataset](https://github.com/eric-epstein/oops-dataset)  
+  - [UCSD Ped2](http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm)  
+  - [CUHK Avenue](https://www.cse.cuhk.edu.hk/leojia/projects/detectabnormal/dataset.html)  
+  - [UCF-Crime](https://www.crcv.ucf.edu/projects/real-world-anomaly-detection-in-surveillance-videos/)
+
+These videos showcase anomalous activities like accidents, social violations, and unexpected events.
+
+### Surreal
+
+- **Source:**  
+  - [SURREAL Dataset](https://www.di.ens.fr/willow/research/surreal/)  
+    Photorealistic synthetic human renderings performing various motions.
+
+### Sci-fi, Animation, Theatre
+
+- **Source:**  
+  - Public YouTube videos, including:
+    - Sci-fi film trailers with futuristic or supernatural elements
+    - Cinematic animations with stylized visuals
+    - Stage performances with exaggerated theatrical expressions
+
+## Data Preprocessing
+
+- **Temporal Segmentation:**  
+  Long videos were manually segmented into 2–10 second clips to isolate key moments and remove redundancy.
+
+- **Content Filtering:**  
+  Non-relevant or noisy content (e.g., intros, subtitles, logos) was excluded to focus on core visual semantics.
+
+- **Resolution Normalization:**  
+  All clips were resized to a consistent format (e.g., 720p) and re-encoded using standard codecs (H.264, MP4) for compatibility.
+
+- **Metadata Annotation:**  
+  Each clip includes metadata describing the source type (e.g., `anomaly`, `surreal`, `sci-fi`), duration, and original dataset/video ID for traceability.
+
+## Download This Repo
+
+You can use the following code to download the entire dataset:
+
+```python
+from huggingface_hub import snapshot_download
+
+repo_id = "your_username/atypical_video_dataset"
+
+snapshot_download(repo_id=repo_id, repo_type="dataset", token={your_token})