---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: output_dir
  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: 0.575
---

<!-- 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. -->

# output_dir

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: 1.2775
- Accuracy: 0.575

## 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: 0.0007
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 31

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 2    | 2.0745          | 0.1125   |
| No log        | 2.0   | 5    | 1.9646          | 0.1875   |
| No log        | 2.8   | 7    | 1.8686          | 0.325    |
| 1.9551        | 4.0   | 10   | 1.7196          | 0.3937   |
| 1.9551        | 4.8   | 12   | 1.5011          | 0.4813   |
| 1.9551        | 6.0   | 15   | 1.3693          | 0.4938   |
| 1.9551        | 6.8   | 17   | 1.4287          | 0.4625   |
| 1.3855        | 8.0   | 20   | 1.2961          | 0.5188   |
| 1.3855        | 8.8   | 22   | 1.2534          | 0.5312   |
| 1.3855        | 10.0  | 25   | 1.2544          | 0.5      |
| 1.3855        | 10.8  | 27   | 1.2417          | 0.5437   |
| 0.8352        | 12.0  | 30   | 1.1863          | 0.5437   |
| 0.8352        | 12.8  | 32   | 1.2524          | 0.5437   |
| 0.8352        | 14.0  | 35   | 1.3570          | 0.5062   |
| 0.8352        | 14.8  | 37   | 1.3046          | 0.5687   |
| 0.4513        | 16.0  | 40   | 1.3582          | 0.4688   |
| 0.4513        | 16.8  | 42   | 1.3063          | 0.5625   |
| 0.4513        | 18.0  | 45   | 1.3494          | 0.5312   |
| 0.4513        | 18.8  | 47   | 1.2484          | 0.5938   |
| 0.282         | 20.0  | 50   | 1.3694          | 0.5437   |
| 0.282         | 20.8  | 52   | 1.4651          | 0.5375   |
| 0.282         | 22.0  | 55   | 1.3577          | 0.5563   |
| 0.282         | 22.8  | 57   | 1.2522          | 0.5625   |
| 0.2038        | 24.0  | 60   | 1.4027          | 0.5813   |
| 0.2038        | 24.8  | 62   | 1.2445          | 0.5938   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3