---
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_batch64_lr0.001_size224_tiles1_seed1_vit_old_transform_old_hp
  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: 0.7538666666666667
---

<!-- 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_batch64_lr0.001_size224_tiles1_seed1_vit_old_transform_old_hp

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: 0.5220
- Accuracy: 0.7539

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6668        | 1.0   | 235  | 0.6653          | 0.5725   |
| 0.6527        | 2.0   | 470  | 0.6233          | 0.6528   |
| 0.5628        | 3.0   | 705  | 0.5658          | 0.7048   |
| 0.4683        | 4.0   | 940  | 0.5314          | 0.7291   |
| 0.3694        | 5.0   | 1175 | 0.5220          | 0.7539   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1