---
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-weight-decay-1e-2
  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-weight-decay-1e-2

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: 0.4994
- Accuracy: 0.8603
- Precision: 0.8618
- Recall: 0.8603
- F1: 0.8600

## 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: 1219
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7124        | 1.0   | 321  | 0.8697          | 0.6924   | 0.6656    | 0.6924 | 0.6030 |
| 1.1476        | 2.0   | 642  | 0.7271          | 0.6990   | 0.7684    | 0.6990 | 0.7149 |
| 1.0734        | 3.0   | 963  | 0.6441          | 0.7687   | 0.7568    | 0.7687 | 0.7417 |
| 1.0271        | 4.0   | 1284 | 0.5855          | 0.7774   | 0.7883    | 0.7774 | 0.7814 |
| 0.9158        | 5.0   | 1605 | 0.7002          | 0.7635   | 0.7930    | 0.7635 | 0.7662 |
| 0.9167        | 6.0   | 1926 | 0.5867          | 0.7812   | 0.8065    | 0.7812 | 0.7900 |
| 0.786         | 7.0   | 2247 | 0.6517          | 0.7340   | 0.8047    | 0.7340 | 0.7515 |
| 0.7406        | 8.0   | 2568 | 0.6647          | 0.7067   | 0.8134    | 0.7067 | 0.7330 |
| 0.682         | 9.0   | 2889 | 0.5106          | 0.8228   | 0.8231    | 0.8228 | 0.8207 |
| 0.6427        | 10.0  | 3210 | 0.5032          | 0.8166   | 0.8354    | 0.8166 | 0.8222 |
| 0.5663        | 11.0  | 3531 | 0.5358          | 0.8152   | 0.8326    | 0.8152 | 0.8216 |
| 0.5395        | 12.0  | 3852 | 0.5488          | 0.8249   | 0.8392    | 0.8249 | 0.8299 |
| 0.4468        | 13.0  | 4173 | 0.5790          | 0.8232   | 0.8397    | 0.8232 | 0.8260 |
| 0.4247        | 14.0  | 4494 | 0.5438          | 0.8415   | 0.8570    | 0.8415 | 0.8449 |
| 0.3495        | 15.0  | 4815 | 0.5135          | 0.8454   | 0.8519    | 0.8454 | 0.8467 |
| 0.3039        | 16.0  | 5136 | 0.5631          | 0.8408   | 0.8520    | 0.8408 | 0.8448 |
| 0.2602        | 17.0  | 5457 | 0.4994          | 0.8603   | 0.8618    | 0.8603 | 0.8600 |
| 0.2616        | 18.0  | 5778 | 0.5406          | 0.8564   | 0.8622    | 0.8564 | 0.8585 |
| 0.1876        | 19.0  | 6099 | 0.5612          | 0.8481   | 0.8629    | 0.8481 | 0.8525 |
| 0.2052        | 20.0  | 6420 | 0.6803          | 0.8429   | 0.8502    | 0.8429 | 0.8428 |
| 0.1533        | 21.0  | 6741 | 0.5464          | 0.8734   | 0.8698    | 0.8734 | 0.8709 |
| 0.1175        | 22.0  | 7062 | 0.5573          | 0.8686   | 0.8667    | 0.8686 | 0.8673 |
| 0.1218        | 23.0  | 7383 | 0.6043          | 0.8703   | 0.8681    | 0.8703 | 0.8669 |
| 0.114         | 24.0  | 7704 | 0.5945          | 0.8710   | 0.8706    | 0.8710 | 0.8693 |
| 0.104         | 25.0  | 8025 | 0.5850          | 0.8766   | 0.8753    | 0.8766 | 0.8752 |
| 0.0752        | 26.0  | 8346 | 0.5868          | 0.8783   | 0.8747    | 0.8783 | 0.8757 |
| 0.1309        | 27.0  | 8667 | 0.5839          | 0.8786   | 0.8753    | 0.8786 | 0.8761 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2