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
model-index:
- name: vit-dropout-v7
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-dropout-v7

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: 0.4366
- Accuracy: 0.8464

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure
- dropout: 0.27
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- 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: 500
- num_epochs: 14
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4353        | 1.56  | 500  | 0.6365          | 0.8040   |
| 0.2729        | 3.12  | 1000 | 0.5354          | 0.8165   |
| 0.3124        | 4.67  | 1500 | 0.4366          | 0.8464   |
| 0.305         | 6.23  | 2000 | 0.4798          | 0.8483   |
| 0.2081        | 7.79  | 2500 | 0.4606          | 0.8689   |
| 0.1876        | 9.35  | 3000 | 0.4844          | 0.8695   |
| 0.1534        | 10.9  | 3500 | 0.5086          | 0.8620   |
| 0.1245        | 12.46 | 4000 | 0.5064          | 0.8670   |


### Framework versions

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