beit-base-patch16-224-pt22k-ft22k-finetuned-stroke-binary Open-Source Model - Efficiently Assist in Stroke Image Classification

Beit Base Patch16 224 Pt22k Ft22k Finetuned Stroke Binary

Developed by BTX24

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on a binary stroke detection dataset for image classification tasks, achieving an evaluation accuracy of 92.22%.

Image Classification

Transformers

Open Source License:Apache-2.0 #Stroke Detection #Medical Image Classification #High-Precision BEIT

Downloads 36

Release Time : 3/16/2025

Model Overview

This is a vision Transformer model based on the BEiT architecture, specifically fine-tuned for binary stroke detection tasks. It can identify stroke signs from medical images and demonstrates high accuracy and F1 scores on test sets.

Model Features

High-Precision Stroke Detection

Achieves 92.22% accuracy and 92.14% F1 score on the test set, demonstrating excellent binary classification performance.

Based on BEiT Architecture

Utilizes an advanced vision Transformer architecture for efficient feature extraction through image patch processing.

Optimized for Medical Imaging

Specially optimized and fine-tuned for the characteristics of medical images in stroke detection.

Model Capabilities

Medical Image Analysis

Binary Task Processing

Stroke Sign Identification

Use Cases

Medical Diagnosis

Stroke Auxiliary Diagnosis

Automatically detects stroke signs from medical images to assist doctors in rapid diagnosis.

Test set accuracy of 92.22%, making it a reliable auxiliary diagnostic tool.

Medical Research

Stroke Case Analysis

Used for automated screening and analysis of large-scale stroke cases.

🚀 beit-base-patch16-224-pt22k-ft22k-finetuned-stroke-binary

This model is a fine - tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on an "Binary Stroke Detection" dataset. It is designed to solve the problem of binary stroke detection in images, providing high - accuracy results for medical image analysis.

🚀 Quick Start

This model is a fine - tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on an "Binary Stroke Detection" dataset. It achieves the following results on the evaluation set:

Loss: 0.2029
Accuracy: 0.9222
F1: 0.9214
Precision: 0.9234
Recall: 0.9222

✨ Features

High - accuracy binary stroke detection on the evaluation set.
Fine - tuned from a well - known base model, microsoft/beit-base-patch16-224-pt22k-ft22k.

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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_with_restarts
lr_scheduler_warmup_ratio: 0.1
num_epochs: 48
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.7256	1.2477	100	0.6913	0.5685	0.4823	0.4731	0.5685
0.6695	2.4954	200	0.6480	0.6210	0.5164	0.5987	0.6210
0.5963	3.7430	300	0.5882	0.6725	0.6118	0.6993	0.6725
0.518	4.9907	400	0.4990	0.7481	0.7167	0.7891	0.7481
0.4325	6.2477	500	0.4090	0.8073	0.7957	0.8232	0.8073
0.3848	7.4954	600	0.3703	0.8340	0.8257	0.8482	0.8340
0.3532	8.7430	700	0.3958	0.8313	0.8201	0.8564	0.8313
0.3297	9.9907	800	0.3257	0.8611	0.8558	0.8718	0.8611
0.3281	11.2477	900	0.3169	0.8666	0.8612	0.8791	0.8666
0.2938	12.4954	1000	0.2814	0.8865	0.8841	0.8900	0.8865
0.2866	13.7430	1100	0.2828	0.8869	0.8837	0.8943	0.8869
0.2884	14.9907	1200	0.2929	0.8847	0.8810	0.8936	0.8847
0.2808	16.2477	1300	0.2458	0.9014	0.8999	0.9034	0.9014
0.258	17.4954	1400	0.2351	0.9091	0.9080	0.9102	0.9091
0.2744	18.7430	1500	0.2516	0.9014	0.8994	0.9057	0.9014
0.261	19.9907	1600	0.2453	0.9068	0.9050	0.9107	0.9068
0.2519	21.2477	1700	0.2564	0.8987	0.8961	0.9051	0.8987
0.2595	22.4954	1800	0.2318	0.9095	0.9079	0.9129	0.9095
0.2548	23.7430	1900	0.2196	0.9136	0.9128	0.9142	0.9136
0.2327	24.9907	2000	0.2376	0.9068	0.9050	0.9110	0.9068
0.2563	26.2477	2100	0.2421	0.9028	0.9005	0.9083	0.9028
0.2348	27.4954	2200	0.2213	0.9109	0.9095	0.9132	0.9109
0.2427	28.7430	2300	0.2308	0.9077	0.9060	0.9116	0.9077
0.2166	29.9907	2400	0.2152	0.9141	0.9128	0.9165	0.9141
0.2345	31.2477	2500	0.2283	0.9068	0.9049	0.9114	0.9068
0.2355	32.4954	2600	0.2173	0.9118	0.9103	0.9149	0.9118
0.2291	33.7430	2700	0.2149	0.9127	0.9113	0.9155	0.9127
0.2319	34.9907	2800	0.2123	0.9141	0.9127	0.9167	0.9141
0.222	36.2477	2900	0.2053	0.9181	0.9171	0.9197	0.9181
0.2235	37.4954	3000	0.2121	0.9141	0.9127	0.9166	0.9141
0.2221	38.7430	3100	0.2013	0.9195	0.9188	0.9200	0.9195
0.2262	39.9907	3200	0.2029	0.9222	0.9214	0.9234	0.9222
0.2171	41.2477	3300	0.2075	0.9181	0.9170	0.9202	0.9181
0.2268	42.4954	3400	0.2045	0.9190	0.9180	0.9208	0.9190
0.2222	43.7430	3500	0.2050	0.9204	0.9194	0.9222	0.9204
0.2169	44.9907	3600	0.2070	0.9177	0.9165	0.9197	0.9177
0.2245	46.2477	3700	0.2064	0.9181	0.9170	0.9201	0.9181
0.2148	47.4954	3800	0.2066	0.9181	0.9170	0.9201	0.9181

Framework versions

Transformers 4.48.3
Pytorch 2.6.0+cu124
Datasets 3.4.0
Tokenizers 0.21.0

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📄 License

The model is licensed under the apache - 2.0 license.

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