face_synthetics / README.md
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Update model card and license.
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---
dataset_info:
features:
- name: image
dtype: image
- name: image_seg
dtype: image
- name: landmarks
dtype: string
splits:
- name: train
num_bytes: 33730885609.0
num_examples: 100000
download_size: 34096881533
dataset_size: 33730885609.0
---
# Dataset Card for `face_synthetics`
This is a copy of [Microsoft FaceSynthetics dataset](https://github.com/microsoft/FaceSynthetics), uploaded to Hugging Face Datasets for convenience.
Please, refer to the original [license](LICENSE.txt), which we replicate in this repo.
The dataset was uploaded using the following code, which assumes the original `zip` file was uncompressed to `/data/microsoft_face_synthetics`:
```Python
from datasets import Dataset
from pathlib import Path
from PIL import Image
face_synthetics = Path("/data/microsoft_face_synthetics")
def entry_for_id(entry_id):
if type(entry_id) == int:
entry_id = f"{entry_id:06}"
image = Image.open(face_synthetics/f"{entry_id}.png")
image_seg = Image.open(face_synthetics/f"{entry_id}_seg.png")
with open(face_synthetics/f"{entry_id}_ldmks.txt") as f:
landmarks = f.read()
return {
"image": image,
"image_seg": image_seg,
"landmarks": landmarks,
}
def generate_entries():
for x in range(100000):
yield entry_for_id(x)
ds = Dataset.from_generator(generate_entries)
ds.push_to_hub('pcuenq/face_synthetics')
```
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.