---
library_name: transformers
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-finetuned-ISIC-dec2024testepo7
  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: 0.944235770008668
---

<!-- 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. -->

# beit-base-patch16-224-pt22k-ft22k-finetuned-ISIC-dec2024testepo7

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1532
- Accuracy: 0.9442

## 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: 7

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7021        | 0.9985 | 486  | 0.1905          | 0.9214   |
| 0.7584        | 1.9985 | 972  | 0.1713          | 0.9291   |
| 0.5877        | 2.9985 | 1458 | 0.1655          | 0.9333   |
| 0.6842        | 3.9985 | 1944 | 0.1591          | 0.9383   |
| 0.5674        | 4.9985 | 2430 | 0.1506          | 0.9406   |
| 0.5275        | 5.9985 | 2916 | 0.1450          | 0.9439   |
| 0.3942        | 6.9985 | 3402 | 0.1532          | 0.9442   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cpu
- Datasets 3.2.0
- Tokenizers 0.21.0