M

Mit B5

Developed by nvidia
SegFormer is a Transformer-based semantic segmentation model. This version only includes the encoder pretrained on ImageNet-1k.
Downloads 15.94k
Release Time : 3/2/2022

Model Overview

SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decoder head. This model only includes the pretrained hierarchical Transformer encoder, which can be fine-tuned for semantic segmentation tasks.

Model Features

Hierarchical Transformer Architecture
Adopts a hierarchical Transformer design to efficiently process image features at different scales
Lightweight Design
The model is concise and efficient, reducing computational resource requirements while maintaining performance
Pretrained Encoder
Provides an encoder pretrained on ImageNet-1k for easy fine-tuning on downstream tasks

Model Capabilities

Image Classification
Semantic Segmentation (requires fine-tuning)
Feature Extraction

Use Cases

Computer Vision
Semantic Segmentation
Can be used for scene understanding, autonomous driving, and other tasks requiring pixel-level classification
Performs excellently on benchmarks like ADE20K and Cityscapes
Image Classification
Can be directly used for 1000-class ImageNet image classification tasks
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