---
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: 0.5875
---

<!-- 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: 1.2119
- Accuracy: 0.5875

## 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: 5e-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: 41

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 2    | 2.0638          | 0.1562   |
| No log        | 2.0   | 5    | 2.0353          | 0.2      |
| No log        | 2.8   | 7    | 1.9965          | 0.2687   |
| 1.9968        | 4.0   | 10   | 1.9289          | 0.3937   |
| 1.9968        | 4.8   | 12   | 1.8942          | 0.3125   |
| 1.9968        | 6.0   | 15   | 1.8054          | 0.4562   |
| 1.9968        | 6.8   | 17   | 1.7626          | 0.4313   |
| 1.7555        | 8.0   | 20   | 1.7078          | 0.4562   |
| 1.7555        | 8.8   | 22   | 1.6608          | 0.45     |
| 1.7555        | 10.0  | 25   | 1.6121          | 0.425    |
| 1.7555        | 10.8  | 27   | 1.5759          | 0.4813   |
| 1.5214        | 12.0  | 30   | 1.5340          | 0.4562   |
| 1.5214        | 12.8  | 32   | 1.5006          | 0.5062   |
| 1.5214        | 14.0  | 35   | 1.4956          | 0.4313   |
| 1.5214        | 14.8  | 37   | 1.4418          | 0.5125   |
| 1.3342        | 16.0  | 40   | 1.4236          | 0.525    |
| 1.3342        | 16.8  | 42   | 1.3784          | 0.55     |
| 1.3342        | 18.0  | 45   | 1.4367          | 0.4938   |
| 1.3342        | 18.8  | 47   | 1.3665          | 0.525    |
| 1.1553        | 20.0  | 50   | 1.3867          | 0.4813   |
| 1.1553        | 20.8  | 52   | 1.3536          | 0.5312   |
| 1.1553        | 22.0  | 55   | 1.3391          | 0.5125   |
| 1.1553        | 22.8  | 57   | 1.2930          | 0.5563   |
| 0.9972        | 24.0  | 60   | 1.2894          | 0.5375   |
| 0.9972        | 24.8  | 62   | 1.2802          | 0.5625   |
| 0.9972        | 26.0  | 65   | 1.2671          | 0.5687   |
| 0.9972        | 26.8  | 67   | 1.2491          | 0.5625   |
| 0.838         | 28.0  | 70   | 1.2907          | 0.5437   |
| 0.838         | 28.8  | 72   | 1.2806          | 0.5563   |
| 0.838         | 30.0  | 75   | 1.2228          | 0.5687   |
| 0.838         | 30.8  | 77   | 1.2485          | 0.575    |
| 0.7226        | 32.0  | 80   | 1.2777          | 0.5437   |
| 0.7226        | 32.8  | 82   | 1.2106          | 0.6      |


### Framework versions

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