---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- animals
- vision-transformer
- vit
- transfer-learning
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-90-animals-moreepochs
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: iamsouravbanerjee/animal-image-dataset-90-different-animals
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9851851851851852
---

<!-- 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-90-animals-moreepochs

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the iamsouravbanerjee/animal-image-dataset-90-different-animals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0709
- Accuracy: 0.9852

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1913        | 1.0   | 270  | 0.3072          | 0.9722   |
| 0.2882        | 2.0   | 540  | 0.1545          | 0.9722   |
| 0.1824        | 3.0   | 810  | 0.1328          | 0.9704   |
| 0.1578        | 4.0   | 1080 | 0.1217          | 0.9704   |
| 0.1518        | 5.0   | 1350 | 0.1161          | 0.9704   |
| 0.1246        | 6.0   | 1620 | 0.1134          | 0.9704   |
| 0.1203        | 7.0   | 1890 | 0.1134          | 0.9704   |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1