
Iterating on camera system design is a lengthy process if you have to collect data from real hardware for each iteration. Instead, our synthetic data platform enables you to understand model performance trade-offs in software. Simulate camera placement, image modality, resolution, and more to inform the overall design of your computer vision system–all without touching a single wire.
1000x Faster Turnaround.
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.
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.
