Update README: schema, splits, dedup note, label table

README.md

@@ -3,6 +3,14 @@ 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:
@@ -15,76 +23,204 @@ configs:
 ---
 # ViSignLanguage-MediaPipe
 
-MediaPipe Holistic 3D keypoint dataset extracted from the Vietnamese Sign Language (**ViSignLanguage**) video dataset.
-
-## Dataset Splits & Shard Mapping
-
-Custom split names (`train`, `public_test`, `private_test`) are configured in the YAML header above so `datasets.load_dataset("Haloroute/ViSignLanguage-MediaPipe")` automatically resolves all splits:
-
-| Split | Total Videos | Extracted (Uploaded) | Failed / Empty |
-| :--- | ---: | ---: | ---: |
-| `train` | 2806 | 2806 | 0 |
-| `public_test` | 1630 | 1630 | 0 |
-| `private_test` | 2859 | 2859 | 0 |
-
-## Schema & Columns
-
-Each row corresponds to a single video clip stored in Arrow/Parquet format:
-
-| Column | Type / Shape | Description |
-| :--- | :--- | :--- |
-| `pose_pts` | `float32[T, 33, 3]` | Normalized 3D body pose landmarks (`x, y, z`). |
-| `pose_conf` | `float32[T, 33]` | Per-landmark visibility score from MediaPipe Pose (`[0.0, 1.0]`, `0.0` when missing). |
-| `face_pts` | `float32[T, 478, 3]` | Normalized 3D face mesh landmarks (`x, y, z`, 478 points including iris). |
-| `face_conf` | `float32[T, 478]` | Presence score (`1.0` when face is detected in frame, `0.0` when missing). |
-| `left_hand_pts` | `float32[T, 21, 3]` | Normalized 3D left-hand landmarks (`x, y, z`). |
-| `left_hand_conf` | `float32[T, 21]` | Presence score (`1.0` when left hand is detected in frame, `0.0` when missing). |
-| `right_hand_pts` | `float32[T, 21, 3]` | Normalized 3D right-hand landmarks (`x, y, z`). |
-| `right_hand_conf` | `float32[T, 21]` | Presence score (`1.0` when right hand is detected in frame, `0.0` when missing). |
-| `transcript` | `string` | NFC-normalized Vietnamese sign gloss for `train`; empty string `""` for test splits. |
-| `fps` | `float32` | Video frame rate (probed via OpenCV, fallback to metadata CSV). |
-| `duration` | `int32` | Number of decoded frames `T`. |
-| `key` | `string` | Unique relative identifier: `train/<label>/<filename>` or `<split>/<filename>`. |
-| `metadata` | `string` | JSON-encoded object (`ensure_ascii=False`) with `label`, `label_id`, `split`, `filename`, and CSV metadata (`size_mb`, `fps`, `total_frames`, `width`, `height`, `duration_s` for `train`). |
-
-## Keypoint Layout
-
-- **Pose**: 33 landmarks (`x, y` normalized to `[0, 1]` image coordinates, `z` relative depth at hips origin). Confidence is the landmark `visibility` probability.
-- **Face**: 478 landmarks (`x, y, z` normalized). Confidence is binary (`1.0` if detected, `0.0` if missing).
-- **Hands (Left / Right)**: 21 landmarks each (`x, y, z` normalized). Confidence is binary (`1.0` if detected, `0.0` if missing).
-- Leading person dimension `(1, T, ...)` from `HolisticPose` is squeezed to `(T, ...)`.
-
-## Label Mapping
-
-The complete 100-class mapping (`label -> int id`, NFC-normalized) is stored in [`label_mapping.json`](label_mapping.json).
-
-| ID | Label (NFC Vietnamese) |
-| ---: | :--- |
-| 0 | `An ủi` |
-| 1 | `Áp dụng` |
-| 2 | `Ăn` |
-| 3 | `Ăn mừng` |
-| 4 | `Ban ngày` |
-| 5 | `Ban đêm` |
-| 6 | `Bàn tay` |
-| 7 | `Băn khoăn` |
-| 8 | `Bạn thân` |
-| 9 | `Bế mạc` |
-| 10 | `Bệnh nhân` |
-| 11 | `Bệnh viện` |
-| 12 | `Biết` |
-| 13 | `Biếu tặng` |
-| 14 | `Bộ y tế` |
-| ... | *(100 total classes — see `label_mapping.json`)* |
+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")
-sample = ds["train"][0]
+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.