---
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-poly
  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-poly

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: 0.4501
- Accuracy: 0.8488
- Precision: 0.8418
- Recall: 0.8488
- F1: 0.8427

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5905        | 0.31  | 100  | 0.6207          | 0.7788   | 0.7719    | 0.7788 | 0.7700 |
| 0.5605        | 0.62  | 200  | 0.7325          | 0.7621   | 0.7503    | 0.7621 | 0.7152 |
| 0.7068        | 0.93  | 300  | 0.5869          | 0.7920   | 0.8184    | 0.7920 | 0.7952 |
| 0.3773        | 1.25  | 400  | 0.5412          | 0.7854   | 0.8200    | 0.7854 | 0.7964 |
| 0.3501        | 1.56  | 500  | 0.5548          | 0.8214   | 0.8133    | 0.8214 | 0.8029 |
| 0.31          | 1.87  | 600  | 0.6007          | 0.7881   | 0.8345    | 0.7881 | 0.7906 |
| 0.1492        | 2.18  | 700  | 0.4845          | 0.8370   | 0.8433    | 0.8370 | 0.8340 |
| 0.185         | 2.49  | 800  | 0.4501          | 0.8488   | 0.8418    | 0.8488 | 0.8427 |
| 0.2438        | 2.8   | 900  | 0.4976          | 0.8440   | 0.8412    | 0.8440 | 0.8338 |
| 0.0604        | 3.12  | 1000 | 0.5850          | 0.8408   | 0.8425    | 0.8408 | 0.8405 |
| 0.0545        | 3.43  | 1100 | 0.5685          | 0.8492   | 0.8476    | 0.8492 | 0.8445 |
| 0.0719        | 3.74  | 1200 | 0.6311          | 0.8523   | 0.8490    | 0.8523 | 0.8445 |
| 0.0809        | 4.05  | 1300 | 0.5321          | 0.8561   | 0.8515    | 0.8561 | 0.8528 |
| 0.0259        | 4.36  | 1400 | 0.8158          | 0.8408   | 0.8329    | 0.8408 | 0.8288 |
| 0.0586        | 4.67  | 1500 | 0.7028          | 0.8315   | 0.8359    | 0.8315 | 0.8249 |
| 0.0218        | 4.98  | 1600 | 0.8059          | 0.8381   | 0.8380    | 0.8381 | 0.8316 |
| 0.0108        | 5.3   | 1700 | 0.7948          | 0.8474   | 0.8484    | 0.8474 | 0.8470 |
| 0.1129        | 5.61  | 1800 | 0.8089          | 0.8426   | 0.8492    | 0.8426 | 0.8431 |


### Framework versions

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