Open-source stool-condition-classification model - Accurately classify fecal images with an accuracy rate approaching 94.2%

Home

Stool Condition Classification

Developed by hossay

Fine-tuned based on Google's ViT model for fecal image classification with an accuracy of 94.17%

Image Classification

Transformers

Open Source License:Apache-2.0 #Medical image classification #High-accuracy fecal detection #ViT fine-tuned model

Downloads 110

Release Time : 1/3/2024

Model Overview

This model is a fine-tuned version of Google's ViT model on the fecal image dataset, mainly used for the automatic classification of fecal status in the medical and health field.

Model Features

High accuracy

Achieved an accuracy of 94.17% and an F1 score of 93.02% on the evaluation set

Medical application

Designed specifically for fecal status analysis in the medical and health field

Based on ViT architecture

Uses the advanced Vision Transformer architecture for image classification

Model Capabilities

Fecal image classification

Medical image analysis

Health status assessment

Use Cases

Medical and health

Automatic diagnosis of fecal status

Automatically identify and classify the health status of fecal samples

Accuracy 94.17%, F1 score 93.02%

Health monitoring

Used for personal health monitoring and early disease screening

🚀 Stool Condition Classification

This model is a fine - tuned version of [google/vit - base - patch16 - 224](https://huggingface.co/google/vit - base - patch16 - 224) on the stool - image dataset. It offers high - performance image classification for stool conditions, achieving excellent results on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of [google/vit - base - patch16 - 224](https://huggingface.co/google/vit - base - patch16 - 224) on the stool - image dataset. It achieves the following results on the evaluation set:

Loss: 0.4237
Auroc: 0.9418
Accuracy: 0.9417
Sensitivity: 0.9091
Specificty: 0.9661
Ppv: 0.9524
Npv: 0.9344
F1: 0.9302
Model Selection: 0.9215

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Auroc	Accuracy	Sensitivity	Specificty	Ppv	Npv	F1	Model Selection
0.5076	0.98	100	0.5361	0.8538	0.7731	0.5393	0.9801	0.96	0.7061	0.6906	0.5592
0.4086	1.96	200	0.4857	0.8728	0.7836	0.6011	0.9453	0.9068	0.7280	0.7230	0.6558
0.5208	2.94	300	0.5109	0.8059	0.7599	0.6124	0.8905	0.8321	0.7218	0.7055	0.7218
0.474	3.92	400	0.5212	0.8601	0.7995	0.6180	0.9602	0.9322	0.7395	0.7432	0.6578
0.4285	4.9	500	0.4511	0.8728	0.7757	0.7472	0.8010	0.7688	0.7816	0.7578	0.9462
0.3506	5.88	600	0.4716	0.8691	0.8047	0.6798	0.9154	0.8768	0.7635	0.7658	0.7644
0.4239	6.86	700	0.5043	0.8517	0.8100	0.6685	0.9353	0.9015	0.7611	0.7677	0.7332
0.2447	7.84	800	0.5804	0.8592	0.8074	0.6910	0.9104	0.8723	0.7689	0.7712	0.7806
0.1739	8.82	900	0.6225	0.8562	0.8074	0.7135	0.8905	0.8523	0.7783	0.7768	0.8229
0.2888	9.8	1000	0.5807	0.8570	0.8047	0.7528	0.8507	0.8171	0.7953	0.7836	0.9021

Framework versions

Transformers 4.38.2
Pytorch 2.0.1
Datasets 2.14.7
Tokenizers 0.15.2

📄 License

This project is licensed under the Apache - 2.0 license.

Property	Details
Model Type	Fine - tuned version of google/vit - base - patch16 - 224
Training Data	stool - image dataset
Tags	image - classification, generated_from_trainer
Metrics	accuracy, f1

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご