Adapt better

The broadest coverage of computer vision tasks

Not every problem looks like a nail when your toolkit includes more than just a hammer.

4.8

Audi

diabetic retinopathy

branch retinal vein occlusion

tessellation

DOT 0 0 K J E E A A J 3 1 2 2

• Regression
• Multiclass classification
• Multilabel classification
• Object detection
• Quadrilateral detection
• Keypoint detection
• Semantic segmentation
• Instance segmentation
• Scene text recognition
• Depth estimation
• and more...

Enhance your model even further with advanced features like
self-supervised learning, task conversions, multi-task learning, and more.

Dimension exactly

Community-pretrained neural network backbones

One size doesn't fit all, so we let you pick which suits you best.

Mobilenet V3 Small : 0.1 GFLOPs, 67.7% Imagenet accuracy
Mobilenet V3 Large : 0.2 GFLOPs, 74% Imagenet accuracy
Convnext Tiny : 4.5 GFLOPs, 82.5% Imagenet accuracy
Efficientnet V2 Small : 8 GFLOPs, 84.2% Imagenet accuracy
Efficientnet V2 Medium : 25 GFLOPs, 85.1% Imagenet accuracy
Efficientnet V2 Large : 56 GFLOPs, 85.8% Imagenet accuracy
• Mobilenet V3
  Small
• Mobilenet V3
  Large
• Convnext
  Tiny
• Efficientnet V2
  Small
• Efficientnet V2
  Medium
• Efficientnet V2
  Large

We rely on open-source backbones (along with their Imagenet pre-trained weights)
generously provided by Torchvision and Timm.

Integrate easily

Standard data sources and model formats

We fit right in between where data flows in and intelligence springs out.

Check out our human-readable data annotation formats.