---
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: 0.9101851851851852
---

<!-- 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: 0.3224
- Accuracy: 0.9102

## 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: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2269        | 0.37  | 100  | 1.2058          | 0.6019   |
| 1.0891        | 0.74  | 200  | 0.9254          | 0.7046   |
| 0.5328        | 1.11  | 300  | 0.7417          | 0.7741   |
| 0.5259        | 1.48  | 400  | 0.7145          | 0.7722   |
| 0.4889        | 1.85  | 500  | 0.5621          | 0.825    |
| 0.2753        | 2.22  | 600  | 0.5251          | 0.8444   |
| 0.2569        | 2.59  | 700  | 0.5792          | 0.8259   |
| 0.2251        | 2.96  | 800  | 0.4169          | 0.8731   |
| 0.086         | 3.33  | 900  | 0.4182          | 0.8843   |
| 0.1352        | 3.7   | 1000 | 0.3711          | 0.8880   |
| 0.0608        | 4.07  | 1100 | 0.3430          | 0.9046   |
| 0.0175        | 4.44  | 1200 | 0.3241          | 0.9185   |
| 0.0149        | 4.81  | 1300 | 0.3224          | 0.9102   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2