Datasets:
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Browse files- .gitattributes +0 -6
- README.md +20 -48
- data/game/test-00000-of-00001.parquet +3 -0
- data/game/train-00000-of-00001.parquet +3 -0
- data/game/validation-00000-of-00001.parquet +3 -0
- data/non-game/test-00000-of-00001.parquet +3 -0
- data/non-game/train-00000-of-00001.parquet +3 -0
- data/non-game/validation-00000-of-00001.parquet +3 -0
.gitattributes
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*.parquet filter=lfs diff=lfs merge=lfs -text
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data/game/test-00000.tar filter=lfs diff=lfs merge=lfs -text
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data/game/train-00000.tar filter=lfs diff=lfs merge=lfs -text
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README.md
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- config_name: game
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data_files:
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- split: train
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path: data/game/train-*.
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- split: validation
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path: data/game/validation-*.
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- split: test
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path: data/game/test-*.
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- config_name: non-game
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data_files:
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- split: train
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path: data/non-game/train-*.
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- split: validation
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path: data/non-game/validation-*.
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- split: test
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path: data/non-game/test-*.
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---
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# PopSign Images Dataset
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## Dataset Structure
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This dataset uses the WebDataset format, storing samples in tar archives with JPEG-compressed images for efficient storage and streaming.
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### Features
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| File | Type | Description |
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|------|------|-------------|
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| `{id}.json` | JSON | Metadata: file, start, end, text, num_frames |
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| `{id}.pyd` | Pickle | List of JPEG-encoded frame bytes |
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### JSON Metadata Fields
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| Field | Type | Description |
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|-------|------|-------------|
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| `file` | string | Original video file path |
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| `start` |
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| `end` |
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| `text` | string | The English gloss/label for the sign |
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| `
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### Frame Extraction
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## Usage
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### Using WebDataset (recommended for training)
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```python
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import webdataset as wds
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import json
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import pickle
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from PIL import Image
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import io
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# Stream directly from HuggingFace Hub
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url = "https://huggingface.co/datasets/sign/popsign-images/resolve/main/data/game/train-00000.tar"
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dataset = wds.WebDataset(url)
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for sample in dataset:
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# Parse metadata
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metadata = json.loads(sample["json"])
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print(f"Sign: {metadata['text']}, Frames: {metadata['num_frames']}")
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# Load frames from pickle (list of JPEG bytes)
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frame_bytes = pickle.loads(sample["pyd"])
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frames = [Image.open(io.BytesIO(b)) for b in frame_bytes]
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break
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```
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### Using HuggingFace datasets
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```python
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from datasets import load_dataset
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# Load the non-game subset
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non_game_dataset = load_dataset("sign/popsign-images", "non-game")
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```
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## Data Processing
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- config_name: game
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data_files:
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- split: train
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path: data/game/train-*.parquet
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- split: validation
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path: data/game/validation-*.parquet
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- split: test
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path: data/game/test-*.parquet
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- config_name: non-game
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data_files:
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- split: train
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path: data/non-game/train-*.parquet
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- split: validation
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path: data/non-game/validation-*.parquet
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- split: test
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path: data/non-game/test-*.parquet
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---
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# PopSign Images Dataset
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## Dataset Structure
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### Features
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| Column | Type | Description |
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|--------|------|-------------|
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| `file` | string | Original video file path |
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| `start` | float32 | Start time of the sign segment (seconds) |
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| `end` | float32 | End time of the sign segment (seconds) |
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| `text` | string | The English gloss/label for the sign |
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| `images` | list[Image] | Sequence of frames extracted from the video at 256x256 resolution |
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### Frame Extraction
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## Usage
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```python
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from datasets import load_dataset
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# Load the non-game subset
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non_game_dataset = load_dataset("sign/popsign-images", "non-game")
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# Access a sample
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sample = game_dataset["train"][0]
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print(f"Sign: {sample['text']}")
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print(f"Duration: {sample['end'] - sample['start']:.2f}s")
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print(f"Number of frames: {len(sample['images'])}")
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# Display first frame
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sample['images'][0].show()
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```
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## Data Processing
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size 16509843
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data/game/train-00000-of-00001.parquet
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data/game/validation-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 13457718
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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