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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-16-thesis-demo-ISIC-multi-class
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/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
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