---
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-linear
  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-linear

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: 0.4920
- Accuracy: 0.8322
- Precision: 0.8400
- Recall: 0.8322
- F1: 0.8323

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6029        | 0.31  | 100  | 0.6126          | 0.7805   | 0.7602    | 0.7805 | 0.7529 |
| 0.5726        | 0.62  | 200  | 0.6950          | 0.7649   | 0.7613    | 0.7649 | 0.7177 |
| 0.6521        | 0.93  | 300  | 0.5102          | 0.8124   | 0.8149    | 0.8124 | 0.8060 |
| 0.3803        | 1.25  | 400  | 0.6125          | 0.7843   | 0.8128    | 0.7843 | 0.7934 |
| 0.4048        | 1.56  | 500  | 0.5059          | 0.8214   | 0.8156    | 0.8214 | 0.8078 |
| 0.2939        | 1.87  | 600  | 0.6723          | 0.7680   | 0.8366    | 0.7680 | 0.7818 |
| 0.2138        | 2.18  | 700  | 0.6351          | 0.8128   | 0.8480    | 0.8128 | 0.8170 |
| 0.2615        | 2.49  | 800  | 0.4920          | 0.8322   | 0.8400    | 0.8322 | 0.8323 |
| 0.2125        | 2.8   | 900  | 0.5596          | 0.8492   | 0.8509    | 0.8492 | 0.8432 |
| 0.0768        | 3.12  | 1000 | 0.8239          | 0.8291   | 0.8500    | 0.8291 | 0.8235 |
| 0.0649        | 3.43  | 1100 | 0.6827          | 0.8367   | 0.8481    | 0.8367 | 0.8360 |
| 0.1382        | 3.74  | 1200 | 0.6838          | 0.8450   | 0.8467    | 0.8450 | 0.8399 |
| 0.0486        | 4.05  | 1300 | 0.6367          | 0.8578   | 0.8548    | 0.8578 | 0.8494 |
| 0.1122        | 4.36  | 1400 | 0.7330          | 0.8398   | 0.8368    | 0.8398 | 0.8330 |
| 0.0302        | 4.67  | 1500 | 0.7137          | 0.8450   | 0.8470    | 0.8450 | 0.8442 |
| 0.0462        | 4.98  | 1600 | 0.8198          | 0.8516   | 0.8519    | 0.8516 | 0.8456 |
| 0.0109        | 5.3   | 1700 | 0.8482          | 0.8478   | 0.8384    | 0.8478 | 0.8378 |
| 0.0545        | 5.61  | 1800 | 0.8046          | 0.8499   | 0.8547    | 0.8499 | 0.8506 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2