transformers tensorboard safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224-in21k license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-augment-v3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-augment-v3

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the SkinCancerClassification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4122
- Accuracy: {'accuracy': 0.8682896379525593}
- Precision: {'precision': 0.7891363629481224}
- Recall: {'recall': 0.702147611641084}
- F1: {'f1': 0.7241023904492959}

## 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: 0.0002
- 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
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | Precision                         | Recall                         | F1                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------------:|:------------------------------:|:--------------------------:|
| 0.3588        | 1.0   | 321  | 0.5097          | {'accuracy': 0.8177278401997503} | {'precision': 0.6458951735407427} | {'recall': 0.6505053236299779} | {'f1': 0.638516645464825}  |
| 0.3331        | 2.0   | 642  | 0.4549          | {'accuracy': 0.8445692883895131} | {'precision': 0.7630341246189177} | {'recall': 0.6706409138231197} | {'f1': 0.6927226829652856} |
| 0.2535        | 3.0   | 963  | 0.4268          | {'accuracy': 0.8651685393258427} | {'precision': 0.792673703587323}  | {'recall': 0.6988841550092643} | {'f1': 0.7201165886292592} |
| 0.1469        | 4.0   | 1284 | 0.4122          | {'accuracy': 0.8682896379525593} | {'precision': 0.7891363629481224} | {'recall': 0.702147611641084}  | {'f1': 0.7241023904492959} |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
