
The Landmarks dataset was designed to encompass a huge variety of facial expressions across varied camera angles and lighting. If your application involves prediction of head pose, this is the dataset you’ve been looking for.
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The average amount spent on single image for full-segmentation is $6.40* – any additional labels cost more above and beyond that. Our synthetic data provides full-segmentation, landmarks, surface normals, and more – for as little as $0.03 per image.
Of course, that’s only the labeling cost. Procuring the images to label is incredibly time-consuming as well. It can take weeks or months to legally collect diverse images of individuals’ faces for most companies. Our datasets are available immediately, and our programmatic API returns generated images and labels in minutes to hours.
*Based on scale.ai pricing, January 2021.
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ML teams
from face_api_dataset import FaceApiDataset, Modality
dataset = FaceApiDataset("test_dataset")
item = dataset[0]
plt.figure(figsize=(20,20))
plt.imshow(item[Modality.RGB])
plt.figure(figsize=(20,20))
landmark_show(item[Modality.RGB], item[Modality.LANDMARKS])
Our technology seamlessly scales in the cloud with our customers’ demands, from R&D phases with small amounts of data to production requirements of terabytes of data.
With everything available via an API, we integrate seamlessly with your workflows from day 1.

If your team needs a little more machine learning muscle, our experts are ready to jump in. We’ll help reduce your time to market, so don’t hesitate to reach out.
