| --- |
| task_categories: |
| - video-classification |
| language: |
| - vi |
| tags: |
| - sign-language |
| - pose-estimation |
| - mediapipe |
| - keypoints |
| pretty_name: ViSignLanguage-MediaPipe |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "data/shard_*-train.parquet" |
| - split: public_test |
| path: "data/shard_*-public_test.parquet" |
| - split: private_test |
| path: "data/shard_*-private_test.parquet" |
| --- |
| # ViSignLanguage-MediaPipe |
|
|
| This dataset holds MediaPipe Holistic 3D keypoints extracted from the **ViSignLanguage** dataset of Vietnamese isolated-sign videos. Each row is one video clip. The rows contain per-frame landmarks for the body pose, the face mesh and both hands, so models can be trained on keypoints without decoding any video. |
|
|
| - **Task:** isolated sign recognition with 100 classes. |
| - **Clips:** 7,295 short clips, each 16–30 frames long, from 224×224 source videos. |
| - **Format:** Parquet (ZSTD), with tensor columns stored as Hugging Face `Array2D`/`Array3D` features. |
|
|
| ## Splits |
|
|
| | Split | Clips | Shards | Labels | Parquet size | |
| | :--- | ---: | ---: | :--- | ---: | |
| | `train` | 2,806 | 8 | ✅ 100 classes (5–52 clips per class) | ~480 MB | |
| | `public_test` | 1,630 | 4 | ❌ unlabeled | ~290 MB | |
| | `private_test` | 2,859 | 6 | ❌ unlabeled | ~504 MB | |
| | **Total** | **7,295** | **18** | | **~1.27 GB** | |
|
|
| - Shards are named `data/shard_<N>-<split>.parquet`. The YAML header maps them to the split names, so `load_dataset` returns all three splits with no extra arguments. |
| - Every source video was extracted successfully: no split has failed or empty clips. |
| - **Deduplication (2026-11-14):** the original `train` split had 3,875 clips. 1,069 of them were duplicates, named `<id>_<n>.mp4` (for example `13326_1.mp4`), whose base clip `<id>.mp4` was also present. Their keypoints were identical to the base clip's, so they were removed. No filename or `key` in the dataset ends in `_<digits>` any more. The test splits contained no duplicates. |
| - The source dataset publishes no labels for the test splits. In those splits `transcript` is `""` and `label_id` is `null`. |
| - Train shards hold 268–370 rows each, and test shards hold up to 500. |
|
|
| ## Schema |
|
|
| `T` is the clip's frame count (its `duration`, 16–30). |
|
|
| | Column | Feature type | Shape | Description | |
| | :--- | :--- | :--- | :--- | |
| | `pose_pts` | `Array3D(float32)` | `(T, 33, 3)` | Body pose landmarks `(x, y, z)` | |
| | `pose_conf` | `Array2D(float32)` | `(T, 33)` | Per-landmark `visibility` from MediaPipe Pose, in `[0, 1]` | |
| | `face_pts` | `Array3D(float32)` | `(T, 478, 3)` | Face mesh landmarks `(x, y, z)`, including iris points | |
| | `face_conf` | `Array2D(float32)` | `(T, 478)` | `1.0` if a face was detected in the frame, else `0.0` | |
| | `left_hand_pts` | `Array3D(float32)` | `(T, 21, 3)` | Left-hand landmarks `(x, y, z)` | |
| | `left_hand_conf` | `Array2D(float32)` | `(T, 21)` | `1.0` if the left hand was detected in the frame, else `0.0` | |
| | `right_hand_pts` | `Array3D(float32)` | `(T, 21, 3)` | Right-hand landmarks `(x, y, z)` | |
| | `right_hand_conf` | `Array2D(float32)` | `(T, 21)` | `1.0` if the right hand was detected in the frame, else `0.0` | |
| | `transcript` | `string` | – | Gloss (class label), NFC-normalized Vietnamese. `""` in the test splits | |
| | `fps` | `float32` | – | Video frame rate, probed with OpenCV (the metadata CSV is the fallback) | |
| | `duration` | `int32` | – | Number of decoded frames, `T` | |
| | `key` | `string` | – | Unique ID: `train/<label>/<filename>` or `<split>/<filename>` | |
| | `metadata` | `string` | – | JSON object; see below | |
|
|
| ### `metadata` JSON |
|
|
| | Field | `train` | Test splits | Description | |
| | :--- | :---: | :---: | :--- | |
| | `label` | ✅ | `""` | Gloss, NFC-normalized (same as `transcript`) | |
| | `label_id` | ✅ | `null` | Integer class ID from [`label_mapping.json`](label_mapping.json) | |
| | `split` | ✅ | ✅ | `train`, `public_test` or `private_test` | |
| | `filename` | ✅ | ✅ | Source video filename, e.g. `133326.mp4` | |
| | `size_mb`, `fps`, `total_frames`, `width`, `height`, `duration_s` | ✅ | – | Copied from the source `dataset_metadata.csv` (train only) | |
|
|
| Example from `train`: |
|
|
| ```json |
| {"label": "An ủi", "label_id": 0, "split": "train", "filename": "133326.mp4", "size_mb": 0.021, "fps": 20.01, "total_frames": 30, "width": 224, "height": 224, "duration_s": 1.499} |
| ``` |
|
|
| ## Keypoint layout and missing data |
|
|
| - **Pose:** 33 landmarks. `x` and `y` are normalized image coordinates in `[0, 1]`, and `z` is relative depth with the hips as origin. Confidence is MediaPipe's per-landmark `visibility`. |
| - **Face:** 478 landmarks (the 468-point mesh plus 10 iris points), with normalized `(x, y, z)`. Confidence is binary. |
| - **Hands:** 21 landmarks per hand, with normalized `(x, y, z)`. Confidence is binary. |
| - **Missing detections:** when a part is not detected in a frame, all of its points for that frame are `0.0` and its confidence is `0.0`. No `NaN` values are used. Mask with `*_conf > 0` rather than checking the coordinates. Hands are often out of frame: in a sample, the left hand was missing in about 40–70% of frames and the right hand in about 30–40%. |
| - The extractor's leading person axis `(1, T, …)` was squeezed out, giving `(T, …)`. |
|
|
| ## Labels |
|
|
| There are 100 classes. The full `label -> id` mapping (NFC-normalized) is in [`label_mapping.json`](label_mapping.json). The table below gives the number of `train` clips per class after deduplication. |
|
|
| <details> |
| <summary>All 100 classes</summary> |
|
|
| | ID | Label | Train clips | |
| | ---: | :--- | ---: | |
| | 0 | An ủi | 24 | |
| | 1 | Áp dụng | 50 | |
| | 2 | Ăn | 43 | |
| | 3 | Ăn mừng | 47 | |
| | 4 | Ban ngày | 15 | |
| | 5 | Ban đêm | 5 | |
| | 6 | Bàn tay | 5 | |
| | 7 | Băn khoăn | 43 | |
| | 8 | Bạn thân | 15 | |
| | 9 | Bế mạc | 26 | |
| | 10 | Bệnh nhân | 5 | |
| | 11 | Bệnh viện | 36 | |
| | 12 | Biết | 46 | |
| | 13 | Biếu tặng | 38 | |
| | 14 | Bộ y tế | 14 | |
| | 15 | Cá | 41 | |
| | 16 | Cách ly | 5 | |
| | 17 | Cám dỗ | 49 | |
| | 18 | Cần | 6 | |
| | 19 | Cảm ơn | 46 | |
| | 20 | Chào | 26 | |
| | 21 | Chân | 48 | |
| | 22 | Chấp nhận | 5 | |
| | 23 | Chạy | 51 | |
| | 24 | Chậm lại | 49 | |
| | 25 | Chiều | 45 | |
| | 26 | Cho | 34 | |
| | 27 | Chúng ta | 12 | |
| | 28 | Con gấu | 41 | |
| | 29 | Có thể | 15 | |
| | 30 | Cơ thể | 5 | |
| | 31 | Cứu | 37 | |
| | 32 | Dạy dỗ | 30 | |
| | 33 | Dễ | 35 | |
| | 34 | Ghét | 23 | |
| | 35 | Giúp | 30 | |
| | 36 | Hâm mộ | 41 | |
| | 37 | Ho | 5 | |
| | 38 | Hôm nay | 17 | |
| | 39 | Họ | 35 | |
| | 40 | Học sinh | 5 | |
| | 41 | Hy sinh | 37 | |
| | 42 | Kết hôn | 23 | |
| | 43 | Khai báo | 40 | |
| | 44 | Khẩu trang | 8 | |
| | 45 | Khóc | 45 | |
| | 46 | Khu cách ly | 9 | |
| | 47 | Lây bệnh | 36 | |
| | 48 | Lo lắng | 5 | |
| | 49 | Mời vào | 40 | |
| | 50 | Mua | 26 | |
| | 51 | Nặng | 34 | |
| | 52 | Nghe | 49 | |
| | 53 | Nghỉ ngơi | 49 | |
| | 54 | Ngón tay | 5 | |
| | 55 | Nhà | 51 | |
| | 56 | Nhầm | 5 | |
| | 57 | Nhìn | 40 | |
| | 58 | Nhớ | 40 | |
| | 59 | Nói | 21 | |
| | 60 | Nói xấu | 50 | |
| | 61 | Nôn ói | 31 | |
| | 62 | Ô tô | 33 | |
| | 63 | Phạt | 26 | |
| | 64 | Phía sau | 49 | |
| | 65 | Phỏng vấn | 32 | |
| | 66 | Phục hồi | 5 | |
| | 67 | Rau | 51 | |
| | 68 | Rẽ phải | 12 | |
| | 69 | Rẽ trái | 47 | |
| | 70 | San sẻ | 33 | |
| | 71 | Sốt | 5 | |
| | 72 | Sử dụng | 44 | |
| | 73 | Tập luyện | 9 | |
| | 74 | Thất lạc | 20 | |
| | 75 | Thăm | 13 | |
| | 76 | Thích | 8 | |
| | 77 | Thương | 5 | |
| | 78 | Thức ăn | 39 | |
| | 79 | Thức dậy | 51 | |
| | 80 | Tôi | 36 | |
| | 81 | Tối | 18 | |
| | 82 | Trưa | 41 | |
| | 83 | Trường học | 52 | |
| | 84 | Uống | 19 | |
| | 85 | Ủng hộ | 5 | |
| | 86 | Vâng lời | 5 | |
| | 87 | Virus | 5 | |
| | 88 | Xa | 42 | |
| | 89 | Xe máy | 5 | |
| | 90 | Xe đạp | 13 | |
| | 91 | Xin lỗi | 14 | |
| | 92 | Xin phép | 28 | |
| | 93 | Xuất viện | 35 | |
| | 94 | Xúc động | 43 | |
| | 95 | Đâu | 39 | |
| | 96 | Đầu | 36 | |
| | 97 | Đẹp | 14 | |
| | 98 | Đi | 46 | |
| | 99 | Đồng ý | 36 | |
|
|
| </details> |
|
|
| ## Usage |
|
|
| ```python |
| import json |
| import numpy as np |
| from datasets import load_dataset |
| |
| ds = load_dataset("Haloroute/ViSignLanguage-MediaPipe") |
| print(ds) # train: 2806, public_test: 1630, private_test: 2859 |
| |
| sample = ds["train"].with_format("numpy")[0] |
| print(sample["key"], sample["transcript"], sample["duration"]) |
| print(sample["right_hand_pts"].shape) # (T, 21, 3) |
| |
| meta = json.loads(sample["metadata"]) |
| label_id = meta["label_id"] |
| |
| # Frames where the right hand was detected |
| rh_valid = sample["right_hand_conf"][:, 0] > 0 |
| ``` |
|
|
| ## Extraction |
|
|
| The keypoints were extracted with the MediaPipe Tasks Holistic Landmarker (`holistic_landmarker.task`) and written as one Parquet row per video. Labels come from the source folder names and were mapped to IDs with the source `label_mapping.pkl`. Train metadata was joined from `dataset_metadata.csv` on `filename`, because that CSV's label and filepath columns are mojibake. |
|
|
