ViSignLanguage-Video

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+# Audio files - uncompressed
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+# Audio files - compressed
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+# Image files - uncompressed
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+# Image files - compressed
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+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
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README.md ADDED Viewed

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+---
+language:
+  - vi
+license: cc-by-4.0
+size_categories:
+  - 1K<n<10K
+task_categories:
+  - video-classification
+tags:
+  [
+    sign-language,
+    vietnamese-sign-language,
+    computer-vision,
+    video-classification,
+  ]
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: dataset_metadata.csv
+---
+# AI Challenge CV Dataset Description
+This dataset comes from [https://aichallenge.ptit.edu.vn/](https://aichallenge.ptit.edu.vn/) and is organized for a multi-class video classification task focusing on Vietnamese Sign Language.
+---
+## 🛠 Dataset Viewer Configuration
+This dataset supports Hugging Face **Dataset Viewer**. The dataset structure and video paths are mapped via `dataset_metadata.csv`.
+### Expected CSV Structure
+To ensure the Dataset Viewer renders correctly, your `dataset_metadata.csv` should follow this format:
+| video_file | label |
+| :--- | :--- |
+| `train/Ăn/video_001.mp4` | `Ăn` |
+| `train/Ban đêm/video_002.mp4` | `Ban đêm` |
+---
+## Dataset Structure
+The `train` folder contains training data organized in a class-based directory structure.
+- each subfolder corresponds to a class label for the classification problem
+- each subfolder name is the class label, for example: `Ăn`, `Ban đêm`, `Bạn thân`, `Bệnh viện`, `Virus`, `Xe đạp`, `Xin lỗi`, ...
+- each folder contains many short MP4 video clips
+## What this means
+- Each subfolder holds examples belonging to that label.
+- The dataset is arranged as a label-directory dataset, which is ideal for multi-class classification tasks.
+- Although the training set is organized by folder labels, the actual examples are short video files in MP4 format.
+## Representative Labels
+Some notable class labels in `train` include:
+- Ăn
+- Ăn mừng
+- An ủi
+- Áp dụng
+- Ban đêm
+- Bạn thân
+- Bệnh viện
+- Bộ y tế
+- Con gấu
+- Cứu
+- Học sinh
+- Khẩu trang
+- Lây bệnh
+- Nghe
+- Nghỉ ngơi
+- Phòng xét nghiệm
+- Sốt
+- Sử dụng
+- Thức ăn
+- Thương
+- Xe đạp
+- Ô tô
+- Xin lỗi
+- Xuất viện
+- ...
+## Summary
+In summary, the training dataset is organized by label directories and is well suited for multi-class classification problems. For a deeper inspection, open each label subfolder to see the number of samples and the exact file formats inside.
+## Sample Visualizations
+### Sample Video Frames
+![Sample frames from the dataset](analysis_dataset/fig12_sample_frames.png)
+### Video Overview
+![Video overview of the dataset](analysis_dataset/fig2_video_overview.png)
+### Label Balance
+![Label balance of the dataset](analysis_dataset/fig9_label_balance.png)
+## Notes for Hugging Face
+This description is intended for use on Hugging Face as an overview of the dataset structure and class organization. Ensure the dataset files and images are uploaded together so these images render correctly in the dataset card.