---
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: 0.9018518518518519
---

<!-- 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: 0.4588
- Accuracy: 0.9019

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1739        | 1.11  | 100  | 0.5771          | 0.8407   |
| 0.0677        | 2.22  | 200  | 0.5907          | 0.8519   |
| 0.0699        | 3.33  | 300  | 0.4160          | 0.8870   |
| 0.0598        | 4.44  | 400  | 0.7336          | 0.8380   |
| 0.0108        | 5.56  | 500  | 0.5133          | 0.8898   |
| 0.0082        | 6.67  | 600  | 0.4786          | 0.8981   |
| 0.0031        | 7.78  | 700  | 0.4624          | 0.9009   |
| 0.0046        | 8.89  | 800  | 0.4594          | 0.9      |
| 0.0052        | 10.0  | 900  | 0.4588          | 0.9019   |


### Framework versions

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