Vit Base Patch16 224 In21k GI Diagnosis

🚀 vit-base-patch16-224-in21k_GI_diagnosis

This is a fine - tuned image classification model for GI diagnosis, achieving high accuracy on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k). It achieves the following results on the evaluation set:

• Loss: 0.2538
• Accuracy: 0.9375
• Weighted f1: 0.9365
• Micro f1: 0.9375
• Macro f1: 0.9365
• Weighted recall: 0.9375
• Micro recall: 0.9375
• Macro recall: 0.9375
• Weighted precision: 0.9455
• Micro precision: 0.9375
• Macro precision: 0.9455

✨ Features

Model description

This is a multiclass image classification model of GI diagnosis'.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Diagnoses%20from%20Colonoscopy%20Images/diagnosis_from_colonoscopy_image_ViT.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/francismon/curated - colon - dataset - for - deep - learning

🔧 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: 3

Training results

Framework versions

• Transformers 4.22.2
• Pytorch 1.12.1
• Datasets 2.5.2
• Tokenizers 0.12.1

📄 License

This model is licensed under the Apache 2.0 license.