M

Mit B3

Developed by nvidia
SegFormer encoder fine-tuned on ImageNet-1k, featuring a hierarchical Transformer architecture, suitable for semantic segmentation tasks.
Downloads 7,136
Release Time : 3/2/2022

Model Overview

SegFormer is a Transformer-based semantic segmentation model with a concise and efficient design. This model includes only the pretrained hierarchical Transformer encoder, which can be fine-tuned for downstream tasks.

Model Features

Hierarchical Transformer Architecture
Adopts a hierarchical Transformer encoder capable of effectively processing visual features at different scales.
Lightweight MLP Decoder Head
Paired with a lightweight all-MLP decoder head architecture, reducing computational complexity while maintaining performance.
ImageNet-1k Pretraining
The encoder has been pretrained on the ImageNet-1k dataset, possessing excellent feature extraction capabilities.

Model Capabilities

Image Feature Extraction
Semantic Segmentation
Vision Task Fine-tuning

Use Cases

Computer Vision
Scene Understanding
Can be used for semantic segmentation tasks on datasets like ADE20K for scene understanding.
Urban Landscape Analysis
Suitable for semantic segmentation on urban landscape datasets such as Cityscapes.
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