levit_128.fb_dist_in1k-finetuned-stroke-binary Open-source Model - Empowering Efficient Binary Classification Detection of Stroke

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Levit 128.fb Dist In1k Finetuned Stroke Binary

Developed by BTX24

A vision Transformer model based on the LeViT-128 architecture, fine-tuned for binary stroke detection tasks

Image Classification

Transformers

Open Source License:Apache-2.0 #Medical Image Classification #Stroke Detection #Lightweight ViT

Downloads 18

Release Time : 3/17/2025

Model Overview

This model is an image classification model fine-tuned on a binary stroke dataset using the LeViT-128 pre-trained on ImageNet-1k, suitable for stroke detection in medical imaging

Model Features

Lightweight Vision Transformer

Based on the LeViT architecture, it reduces computational complexity while maintaining high performance

Medical Imaging Specialization

Optimized for stroke detection tasks, suitable for medical imaging analysis scenarios

Efficient Fine-Tuning

Utilizes transfer learning techniques for efficient fine-tuning with limited medical data

Model Capabilities

Medical Image Classification

Stroke Detection

Binary Task Processing

Use Cases

Medical Diagnosis

Stroke Auxiliary Diagnosis

Assists doctors in stroke diagnosis through medical image analysis

Achieved 85.98% accuracy on the test set

Medical Imaging Screening

Used for preliminary screening of large-scale stroke cases

🚀 levit_128.fb_dist_in1k-finetuned-stroke-binary

This model is a fine - tuned version of timm/levit_128.fb_dist_in1k for binary stroke detection, achieving high accuracy and other good metrics on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of timm/levit_128.fb_dist_in1k on a binary stroke detection dataset. It achieves the following results on the evaluation set:

Accuracy: 0.8598
F1: 0.8577
Precision: 0.8602
Recall: 0.8598

📚 Documentation

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 36
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.7002	0.6202	100	nan	0.5690	0.5387	0.5349	0.5690
0.681	1.2357	200	nan	0.5834	0.5331	0.5372	0.5834
0.6874	1.8558	300	nan	0.6002	0.5596	0.5665	0.6002
0.6774	2.4713	400	nan	0.6124	0.5811	0.5867	0.6124
0.6533	3.0868	500	nan	0.6852	0.6694	0.6767	0.6852
0.6368	3.7070	600	nan	0.7205	0.7153	0.7153	0.7205
0.6196	4.3225	700	nan	0.7603	0.7471	0.7650	0.7603
0.5663	4.9426	800	nan	0.7883	0.7843	0.7864	0.7883
0.5196	5.5581	900	nan	0.8078	0.7972	0.8206	0.8078
0.4704	6.1736	1000	nan	0.8363	0.8317	0.8396	0.8363
0.4715	6.7938	1100	nan	0.8349	0.8292	0.8409	0.8349
0.452	7.4093	1200	nan	0.8503	0.8479	0.8505	0.8503
0.4538	8.0248	1300	nan	0.8598	0.8577	0.8602	0.8598

Framework versions

Transformers 4.48.3
Pytorch 2.6.0+cu124
Datasets 3.4.0
Tokenizers 0.21.0

📄 License

The model is licensed under the apache - 2.0 license.

📦 Information Table

Property	Details
Library Name	transformers
Base Model	timm/levit_128.fb_dist_in1k
Tags	generated_from_trainer
Metrics	accuracy, f1, precision, recall
Model Name	levit_128.fb_dist_in1k - finetuned - stroke - binary
Datasets	BTX24/tekno21 - brain - stroke - dataset - binary
Pipeline Tag	image - classification

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