State of the Art Computer Vision Models

The state-of-the-art represents the best-of-the-best in model performance. Below, explore the models that achieve excellent performance in their respective fields.

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Instance Segmentation
Instance Segmentation
Instance Segmentation
Instance Segmentation

Model Size:

MB

Parameters:

Top FPS:

Architecture:

The state-of-the-art YOLOv8 model comes with support for instance segmentation tasks. Learn more »
Object Detection
Object Detection
Object Detection
Object Detection

Model Size:

MB

Parameters:

Top FPS:

Architecture:

YOLO, CNN

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5. Learn more »
Instance Segmentation
Instance Segmentation
Instance Segmentation
Instance Segmentation

Model Size:

MB

Parameters:

219 million

Top FPS:

Architecture:

Transformers

OneFormer is a state-of-the-art multi-task image segmentation framework that is implemented using transformers. Learn more »
Classification
Classification
Classification
Classification

Model Size:

MB

Parameters:

Top FPS:

Architecture:

The Vision Transformer leverages powerful natural language processing embeddings (BERT) and applies them to images. Learn more »
Semantic Segmentation
Semantic Segmentation
Semantic Segmentation
Semantic Segmentation

Model Size:

MB

Parameters:

Top FPS:

51

Architecture:

Transformers

SegFormer is a computer vision framework used in semantic segmentation tasks, implemented with transformers. Learn more »
Object Detection
Object Detection
Object Detection
Object Detection

Model Size:

75.6

MB

Parameters:

Top FPS:

161

Architecture:

YOLO, CNN

YOLOv7 is a state of the art object detection model. Learn more »
Object Detection
Object Detection
Object Detection
Object Detection

Model Size:

14.1

MB

Parameters:

7.2 million

Top FPS:

140

Architecture:

CNN, YOLO

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results. Learn more »

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