vit-base-patch16-224-in21k_GI_diagnosis

This model is a fine-tuned version of 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

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

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

Training Loss	Epoch	Step	Validation Loss	Accuracy	Weighted f1	Micro f1	Macro f1	Weighted recall	Micro recall	Macro recall	Weighted precision	Micro precision	Macro precision
1.3805	1.0	200	0.5006	0.8638	0.8531	0.8638	0.8531	0.8638	0.8638	0.8638	0.9111	0.8638	0.9111
1.3805	2.0	400	0.2538	0.9375	0.9365	0.9375	0.9365	0.9375	0.9375	0.9375	0.9455	0.9375	0.9455
0.0628	3.0	600	0.5797	0.8812	0.8740	0.8812	0.8740	0.8812	0.8812	0.8813	0.9157	0.8812	0.9157

Framework versions

Transformers 4.22.2
Pytorch 1.12.1
Datasets 2.5.2
Tokenizers 0.12.1