---
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_tiles1_seed1_classic_image_classification_t
  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: 0.9954666666666667
---

<!-- 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_tiles1_seed1_classic_image_classification_t

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: 0.0171
- Accuracy: 0.9955

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.006         | 1.0   | 469  | 0.0248          | 0.9925   |
| 0.0019        | 2.0   | 938  | 0.0275          | 0.9931   |
| 0.0012        | 3.0   | 1407 | 0.0207          | 0.9952   |
| 0.0008        | 4.0   | 1876 | 0.0171          | 0.9955   |
| 0.0007        | 5.0   | 2345 | 0.0181          | 0.9955   |


### Framework versions

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