---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-lr-cosine-warmup
  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-lr-cosine-warmup

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4736
- Accuracy: 0.8395
- Precision: 0.8318
- Recall: 0.8395
- F1: 0.8308

## 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: 0.0001
- 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: cosine
- lr_scheduler_warmup_steps: 770
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.86          | 1.0   | 321  | 0.5250          | 0.8141   | 0.8100    | 0.8141 | 0.8011 |
| 0.4517        | 2.0   | 642  | 0.5117          | 0.8221   | 0.8347    | 0.8221 | 0.8100 |
| 0.3512        | 3.0   | 963  | 0.4736          | 0.8395   | 0.8318    | 0.8395 | 0.8308 |
| 0.2184        | 4.0   | 1284 | 0.4797          | 0.8568   | 0.8536    | 0.8568 | 0.8505 |
| 0.1264        | 5.0   | 1605 | 0.6212          | 0.8547   | 0.8552    | 0.8547 | 0.8530 |
| 0.0687        | 6.0   | 1926 | 0.7659          | 0.8464   | 0.8476    | 0.8464 | 0.8402 |
| 0.0463        | 7.0   | 2247 | 0.8237          | 0.8519   | 0.8546    | 0.8519 | 0.8469 |
| 0.0373        | 8.0   | 2568 | 0.8712          | 0.8377   | 0.8493    | 0.8377 | 0.8415 |
| 0.0347        | 9.0   | 2889 | 0.8181          | 0.8568   | 0.8550    | 0.8568 | 0.8534 |
| 0.0263        | 10.0  | 3210 | 1.0705          | 0.8447   | 0.8389    | 0.8447 | 0.8308 |
| 0.0289        | 11.0  | 3531 | 0.9376          | 0.8589   | 0.8606    | 0.8589 | 0.8550 |
| 0.0164        | 12.0  | 3852 | 0.9714          | 0.8634   | 0.8611    | 0.8634 | 0.8611 |
| 0.0077        | 13.0  | 4173 | 1.2992          | 0.8398   | 0.8396    | 0.8398 | 0.8243 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2