metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - f1
  - precision
model-index:
  - name: vit-base-16-thesis-demo-ISIC-multi-class
    results: []

vit-base-16-thesis-demo-ISIC-multi-class

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the ahishamm/isic_enhanced_dec_balanced dataset. It achieves the following results on the evaluation set:

Loss: 0.0906
Accuracy: 0.9748
Recall: 0.9748
F1: 0.9748
Precision: 0.9748

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: 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: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	F1	Precision
0.575	0.98	50	0.4132	0.8491	0.8491	0.8491	0.8491
0.2771	1.96	100	0.2329	0.9182	0.9182	0.9182	0.9182
0.1703	2.94	150	0.1821	0.9497	0.9497	0.9497	0.9497
0.1186	3.92	200	0.0906	0.9748	0.9748	0.9748	0.9748

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0