---
license: apache-2.0
base_model: akahana/vit-base-cats-vs-dogs
tags:
- generated_from_trainer
datasets:
- cats_vs_dogs
metrics:
- accuracy
model-index:
- name: cat_or_dogs
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cats_vs_dogs
      type: cats_vs_dogs
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9820589491670226
---

<!-- 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. -->

# cat_or_dogs

This model is a fine-tuned version of [akahana/vit-base-cats-vs-dogs](https://huggingface.co/akahana/vit-base-cats-vs-dogs) on the cats_vs_dogs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0561
- Accuracy: 0.9821

## 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: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0389        | 1.0   | 1171 | 0.0638          | 0.9793   |
| 0.0682        | 2.0   | 2342 | 0.0510          | 0.9812   |
| 0.0623        | 3.0   | 3513 | 0.0561          | 0.9821   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2