---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: xyz
  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: 0.9175925925925926
---

<!-- 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. -->

# xyz

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3201
- Accuracy: 0.9176

## 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: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6539        | 1.11  | 100  | 0.8063          | 0.75     |
| 0.2553        | 2.22  | 200  | 0.5555          | 0.8352   |
| 0.1909        | 3.33  | 300  | 0.5217          | 0.8454   |
| 0.0999        | 4.44  | 400  | 0.5075          | 0.8722   |
| 0.0666        | 5.56  | 500  | 0.4633          | 0.8769   |
| 0.0392        | 6.67  | 600  | 0.4614          | 0.8741   |
| 0.0111        | 7.78  | 700  | 0.3574          | 0.9102   |
| 0.0122        | 8.89  | 800  | 0.3159          | 0.9167   |
| 0.0112        | 10.0  | 900  | 0.3201          | 0.9176   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0