--- 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.