histo_train_segformer Open-source Image Classification Model - Fine-tuned on Dataset with 87.5% Accuracy

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Histo Train Segformer

Developed by tcvrishank

Image classification model based on SegFormer architecture, fine-tuned on imagefolder dataset with 87.5% accuracy

Image Classification

Transformers

Open Source License:Other #Medical Image Classification #High-precision Segmentation #Histopathological Analysis

Downloads 14

Release Time : 3/25/2023

Model Overview

This model is a fine-tuned version of nvidia/mit-b0 pre-trained model for image classification tasks, primarily used for histopathological image analysis

Model Features

High Accuracy

Achieves 87.5% classification accuracy on the evaluation set

Efficient Training

Uses mixed precision training (native AMP) to optimize training efficiency

Transfer Learning

Fine-tuned based on nvidia/mit-b0 pre-trained model, fully utilizing pre-trained knowledge

Model Capabilities

Image Classification

Histopathological Image Analysis

Use Cases

Medical Imaging

Pathological Slide Classification

Classification and recognition of histopathological images

87.5% accuracy

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2234	16.67	100	0.3830	0.875

Property	Details
Model Type	Fine - tuned version of nvidia/mit-b0
Training Data	imagefolder
Metrics	accuracy
Results	Task: Image Classification Dataset: imagefolder (train split) Accuracy: 0.875

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Histo Train Segformer

Model Overview

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🚀 histo_train_segformer

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