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-v11
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-dropout-v11

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: 0.5383
- Accuracy: 0.8508

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure
- dropout: 0.35
### 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: 500
- num_epochs: 16
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5972        | 1.56  | 500  | 0.9045          | 0.7160   |
| 0.5053        | 3.12  | 1000 | 0.6760          | 0.7566   |
| 0.5003        | 4.67  | 1500 | 0.5776          | 0.7909   |
| 0.4323        | 6.23  | 2000 | 0.5317          | 0.8084   |
| 0.417         | 7.79  | 2500 | 0.5187          | 0.8277   |
| 0.2751        | 9.35  | 3000 | 0.5660          | 0.8346   |
| 0.3664        | 10.9  | 3500 | 0.5247          | 0.8371   |
| 0.2595        | 12.46 | 4000 | 0.5358          | 0.8546   |
| 0.2009        | 14.02 | 4500 | 0.5289          | 0.8508   |
| 0.1366        | 15.58 | 5000 | 0.5383          | 0.8508   |


### Framework versions

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