---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit_epochs5_batch32_lr5e-05_size224_tiles6_seed2_q2_complexity
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: Dogs_vs_Cats
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.968
---

<!-- 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_epochs5_batch32_lr5e-05_size224_tiles6_seed2_q2_complexity

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 Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1000
- Accuracy: 0.968

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.113         | 1.0   | 469  | 0.1000          | 0.968    |
| 0.0014        | 2.0   | 938  | 0.1242          | 0.9725   |
| 0.0           | 3.0   | 1407 | 0.1503          | 0.972    |
| 0.0           | 4.0   | 1876 | 0.1394          | 0.9752   |
| 0.0           | 5.0   | 2345 | 0.1405          | 0.9749   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.19.1