---
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: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.785
---

<!-- 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: 0.9604
- Accuracy: 0.785

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8634        | 0.55  | 100  | 0.9266          | 0.6975   |
| 0.3225        | 1.09  | 200  | 0.8994          | 0.7325   |
| 0.2353        | 1.64  | 300  | 0.9683          | 0.73     |
| 0.1119        | 2.19  | 400  | 0.9247          | 0.7492   |
| 0.049         | 2.73  | 500  | 0.9663          | 0.7567   |
| 0.0537        | 3.28  | 600  | 1.0558          | 0.7567   |
| 0.0274        | 3.83  | 700  | 1.0344          | 0.7692   |
| 0.0102        | 4.37  | 800  | 0.9259          | 0.7942   |
| 0.0095        | 4.92  | 900  | 0.9604          | 0.785    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0