---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-ISIC-dec2024
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9380236925744004
---

<!-- 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-patch16-224-finetuned-ISIC-dec2024

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1523
- Accuracy: 0.9380

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8152        | 0.9985 | 486  | 0.1791          | 0.9223   |
| 0.6467        | 1.9985 | 972  | 0.1590          | 0.9361   |
| 0.5399        | 2.9985 | 1458 | 0.1523          | 0.9380   |

### Testing results

Testing data confusion values: (Malignant -> Positive, Benign -> Negative)
|               |Predict Positive|Predict Negative|
|:-------------:|:--------------:|:--------------:|
|Actual Positive|1301            |792             |
|Actual Negative|301             |14912           |

- Precision: 0.812
- Recall: 0.622
- Accuracy: 0.937
- F1: 0.704

### Framework versions

- Transformers 4.47.1
- Pytorch 2.6.0.dev20241225+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0