---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-gecko
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.988479262672811
---

<!-- 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. -->

# vit-base-patch16-224-in21k-finetuned-gecko

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: 0.1890
- Accuracy: 0.9885

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.97  | 21   | 3.2699          | 0.6210   |
| No log        | 1.98  | 43   | 2.0011          | 0.8468   |
| 3.1155        | 2.99  | 65   | 1.2851          | 0.8641   |
| 3.1155        | 4.0   | 87   | 0.7751          | 0.9389   |
| 1.1003        | 4.97  | 108  | 0.6060          | 0.9274   |
| 1.1003        | 5.98  | 130  | 0.4584          | 0.9378   |
| 0.5229        | 6.99  | 152  | 0.3417          | 0.9585   |
| 0.5229        | 8.0   | 174  | 0.2415          | 0.9816   |
| 0.5229        | 8.97  | 195  | 0.2014          | 0.9873   |
| 0.3249        | 9.66  | 210  | 0.1890          | 0.9885   |


### Framework versions

- Transformers 4.34.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.6
- Tokenizers 0.14.1