---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ethos-25
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9170896785109983
---

<!-- 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-base-patch16-224-ethos-25

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2803
- Accuracy: 0.9171

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.606         | 0.99  | 43   | 1.3384          | 0.6387   |
| 0.6334        | 1.99  | 86   | 0.5900          | 0.8519   |
| 0.3928        | 2.98  | 129  | 0.4637          | 0.8739   |
| 0.2361        | 4.0   | 173  | 0.3965          | 0.8909   |
| 0.1816        | 4.99  | 216  | 0.4107          | 0.8782   |
| 0.1253        | 5.99  | 259  | 0.3433          | 0.8976   |
| 0.1255        | 6.98  | 302  | 0.3334          | 0.9069   |
| 0.1009        | 8.0   | 346  | 0.3042          | 0.9154   |
| 0.0812        | 8.99  | 389  | 0.2809          | 0.9146   |
| 0.0698        | 9.94  | 430  | 0.2803          | 0.9171   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2