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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: image_seg |
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dtype: image |
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- name: landmarks |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 33730885609.0 |
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num_examples: 100000 |
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download_size: 34096881533 |
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dataset_size: 33730885609.0 |
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--- |
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# Dataset Card for `face_synthetics` |
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This is a copy of [Microsoft FaceSynthetics dataset](https://github.com/microsoft/FaceSynthetics), uploaded to Hugging Face Datasets for convenience. |
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Please, refer to the original [license](LICENSE.txt), which we replicate in this repo. |
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The dataset was uploaded using the following code, which assumes the original `zip` file was uncompressed to `/data/microsoft_face_synthetics`: |
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```Python |
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from datasets import Dataset |
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from pathlib import Path |
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from PIL import Image |
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face_synthetics = Path("/data/microsoft_face_synthetics") |
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def entry_for_id(entry_id): |
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if type(entry_id) == int: |
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entry_id = f"{entry_id:06}" |
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image = Image.open(face_synthetics/f"{entry_id}.png") |
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image_seg = Image.open(face_synthetics/f"{entry_id}_seg.png") |
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with open(face_synthetics/f"{entry_id}_ldmks.txt") as f: |
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landmarks = f.read() |
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return { |
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"image": image, |
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"image_seg": image_seg, |
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"landmarks": landmarks, |
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} |
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def generate_entries(): |
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for x in range(100000): |
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yield entry_for_id(x) |
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ds = Dataset.from_generator(generate_entries) |
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ds.push_to_hub('pcuenq/face_synthetics') |
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``` |
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Note that `image_seg`, the segmented images, appear to be black because each pixel contains a number between `0` to `18` corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet. |
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