---
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: 0.6
---

<!-- 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: 1.2976
- Accuracy: 0.6

## 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: 7e-05
- 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: reduce_lr_on_plateau
- num_epochs: 77

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 2    | 2.0706          | 0.15     |
| No log        | 2.0   | 5    | 2.0309          | 0.2313   |
| No log        | 2.8   | 7    | 1.9846          | 0.2562   |
| 1.9868        | 4.0   | 10   | 1.8915          | 0.4062   |
| 1.9868        | 4.8   | 12   | 1.8529          | 0.3125   |
| 1.9868        | 6.0   | 15   | 1.7422          | 0.4125   |
| 1.9868        | 6.8   | 17   | 1.6761          | 0.4313   |
| 1.6815        | 8.0   | 20   | 1.6310          | 0.4562   |
| 1.6815        | 8.8   | 22   | 1.5900          | 0.45     |
| 1.6815        | 10.0  | 25   | 1.5402          | 0.4313   |
| 1.6815        | 10.8  | 27   | 1.5018          | 0.5      |
| 1.4233        | 12.0  | 30   | 1.4620          | 0.4875   |
| 1.4233        | 12.8  | 32   | 1.4286          | 0.5062   |
| 1.4233        | 14.0  | 35   | 1.4045          | 0.5125   |
| 1.4233        | 14.8  | 37   | 1.3860          | 0.5312   |
| 1.2127        | 16.0  | 40   | 1.3571          | 0.5      |
| 1.2127        | 16.8  | 42   | 1.3293          | 0.5375   |
| 1.2127        | 18.0  | 45   | 1.3742          | 0.4813   |
| 1.2127        | 18.8  | 47   | 1.3151          | 0.5437   |
| 1.0075        | 20.0  | 50   | 1.3053          | 0.5312   |
| 1.0075        | 20.8  | 52   | 1.3266          | 0.5375   |
| 1.0075        | 22.0  | 55   | 1.2964          | 0.5312   |
| 1.0075        | 22.8  | 57   | 1.2278          | 0.5875   |
| 0.8232        | 24.0  | 60   | 1.2501          | 0.5563   |
| 0.8232        | 24.8  | 62   | 1.2330          | 0.575    |
| 0.8232        | 26.0  | 65   | 1.2198          | 0.5625   |
| 0.8232        | 26.8  | 67   | 1.2071          | 0.5875   |
| 0.6738        | 28.0  | 70   | 1.2643          | 0.5875   |
| 0.6738        | 28.8  | 72   | 1.2594          | 0.5563   |
| 0.6738        | 30.0  | 75   | 1.2263          | 0.5312   |
| 0.6738        | 30.8  | 77   | 1.3218          | 0.5188   |
| 0.5715        | 32.0  | 80   | 1.2593          | 0.5312   |
| 0.5715        | 32.8  | 82   | 1.2214          | 0.5625   |
| 0.5715        | 34.0  | 85   | 1.3060          | 0.55     |
| 0.5715        | 34.8  | 87   | 1.2727          | 0.5563   |
| 0.4523        | 36.0  | 90   | 1.2749          | 0.5375   |
| 0.4523        | 36.8  | 92   | 1.3570          | 0.5437   |
| 0.4523        | 38.0  | 95   | 1.2815          | 0.5687   |
| 0.4523        | 38.8  | 97   | 1.2233          | 0.6062   |
| 0.3971        | 40.0  | 100  | 1.2097          | 0.6      |
| 0.3971        | 40.8  | 102  | 1.2881          | 0.5813   |
| 0.3971        | 42.0  | 105  | 1.2400          | 0.575    |
| 0.3971        | 42.8  | 107  | 1.3140          | 0.5375   |
| 0.3616        | 44.0  | 110  | 1.1525          | 0.6125   |
| 0.3616        | 44.8  | 112  | 1.2725          | 0.5938   |
| 0.3616        | 46.0  | 115  | 1.2634          | 0.5813   |
| 0.3616        | 46.8  | 117  | 1.2299          | 0.6      |
| 0.338         | 48.0  | 120  | 1.3408          | 0.5375   |
| 0.338         | 48.8  | 122  | 1.1931          | 0.5938   |
| 0.338         | 50.0  | 125  | 1.2806          | 0.5938   |
| 0.338         | 50.8  | 127  | 1.2410          | 0.575    |
| 0.3445        | 52.0  | 130  | 1.2901          | 0.5813   |
| 0.3445        | 52.8  | 132  | 1.2504          | 0.6062   |
| 0.3445        | 54.0  | 135  | 1.1614          | 0.5875   |
| 0.3445        | 54.8  | 137  | 1.2247          | 0.6062   |
| 0.3299        | 56.0  | 140  | 1.2591          | 0.5625   |
| 0.3299        | 56.8  | 142  | 1.2629          | 0.5687   |
| 0.3299        | 58.0  | 145  | 1.2369          | 0.5938   |
| 0.3299        | 58.8  | 147  | 1.2771          | 0.575    |
| 0.3292        | 60.0  | 150  | 1.3284          | 0.5875   |
| 0.3292        | 60.8  | 152  | 1.2550          | 0.5625   |
| 0.3292        | 61.6  | 154  | 1.3047          | 0.55     |


### Framework versions

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