---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.60625
---

<!-- 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. -->

# image_classification

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1494
- Accuracy: 0.6062

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8347        | 1.0   | 10   | 1.9052          | 0.3688   |
| 1.838         | 2.0   | 20   | 1.7999          | 0.375    |
| 1.7193        | 3.0   | 30   | 1.6869          | 0.35     |
| 1.5873        | 4.0   | 40   | 1.5855          | 0.4437   |
| 1.4919        | 5.0   | 50   | 1.4977          | 0.475    |
| 1.4049        | 6.0   | 60   | 1.4425          | 0.4875   |
| 1.3025        | 7.0   | 70   | 1.4254          | 0.45     |
| 1.238         | 8.0   | 80   | 1.3994          | 0.475    |
| 1.1704        | 9.0   | 90   | 1.3109          | 0.5312   |
| 1.1009        | 10.0  | 100  | 1.3309          | 0.525    |
| 1.0309        | 11.0  | 110  | 1.2941          | 0.5687   |
| 0.9705        | 12.0  | 120  | 1.2750          | 0.5188   |
| 0.9315        | 13.0  | 130  | 1.2402          | 0.55     |
| 0.8894        | 14.0  | 140  | 1.2425          | 0.5375   |
| 0.8374        | 15.0  | 150  | 1.2273          | 0.525    |
| 0.8           | 16.0  | 160  | 1.2454          | 0.5125   |
| 0.7597        | 17.0  | 170  | 1.2445          | 0.5125   |
| 0.7143        | 18.0  | 180  | 1.1750          | 0.5687   |
| 0.6832        | 19.0  | 190  | 1.2456          | 0.525    |
| 0.6573        | 20.0  | 200  | 1.2004          | 0.5938   |
| 0.639         | 21.0  | 210  | 1.1924          | 0.5563   |
| 0.635         | 22.0  | 220  | 1.1257          | 0.6      |
| 0.5982        | 23.0  | 230  | 1.1845          | 0.575    |
| 0.5675        | 24.0  | 240  | 1.2291          | 0.5625   |
| 0.5634        | 25.0  | 250  | 1.1837          | 0.5687   |
| 0.535         | 26.0  | 260  | 1.2384          | 0.5813   |
| 0.5233        | 27.0  | 270  | 1.1911          | 0.5875   |
| 0.529         | 28.0  | 280  | 1.2083          | 0.5875   |
| 0.5141        | 29.0  | 290  | 1.1813          | 0.5875   |
| 0.5166        | 30.0  | 300  | 1.1578          | 0.5938   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3