---
license: apache-2.0
base_model: google/vit-large-patch32-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitLarge-p32-384-2e-4-batch_16_epoch_4_classes_24
  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: 0.978448275862069
---

<!-- 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. -->

# vitLarge-p32-384-2e-4-batch_16_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0894
- Accuracy: 0.9784

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3286        | 0.07  | 100  | 0.3636          | 0.8764   |
| 0.3196        | 0.14  | 200  | 0.4602          | 0.875    |
| 0.1705        | 0.21  | 300  | 0.2507          | 0.9152   |
| 0.2873        | 0.28  | 400  | 0.2614          | 0.9282   |
| 0.0982        | 0.35  | 500  | 0.2327          | 0.9310   |
| 0.1669        | 0.42  | 600  | 0.3202          | 0.9210   |
| 0.1612        | 0.49  | 700  | 0.5062          | 0.8807   |
| 0.1532        | 0.56  | 800  | 0.2240          | 0.9425   |
| 0.1728        | 0.63  | 900  | 0.1601          | 0.9511   |
| 0.167         | 0.7   | 1000 | 0.3861          | 0.9138   |
| 0.0752        | 0.77  | 1100 | 0.2198          | 0.9483   |
| 0.0423        | 0.84  | 1200 | 0.2114          | 0.9440   |
| 0.0898        | 0.91  | 1300 | 0.1613          | 0.9511   |
| 0.0498        | 0.97  | 1400 | 0.2824          | 0.9382   |
| 0.016         | 1.04  | 1500 | 0.1921          | 0.9569   |
| 0.0079        | 1.11  | 1600 | 0.2548          | 0.9382   |
| 0.008         | 1.18  | 1700 | 0.2186          | 0.9497   |
| 0.049         | 1.25  | 1800 | 0.2018          | 0.9569   |
| 0.0333        | 1.32  | 1900 | 0.1676          | 0.9598   |
| 0.1119        | 1.39  | 2000 | 0.1601          | 0.9583   |
| 0.0134        | 1.46  | 2100 | 0.1157          | 0.9741   |
| 0.0192        | 1.53  | 2200 | 0.1320          | 0.9641   |
| 0.0085        | 1.6   | 2300 | 0.1590          | 0.9641   |
| 0.0384        | 1.67  | 2400 | 0.0973          | 0.9741   |
| 0.0531        | 1.74  | 2500 | 0.1719          | 0.9569   |
| 0.0221        | 1.81  | 2600 | 0.1280          | 0.9741   |
| 0.0006        | 1.88  | 2700 | 0.1895          | 0.9540   |
| 0.0006        | 1.95  | 2800 | 0.1258          | 0.9713   |
| 0.016         | 2.02  | 2900 | 0.1105          | 0.9713   |
| 0.0004        | 2.09  | 3000 | 0.1118          | 0.9684   |
| 0.0001        | 2.16  | 3100 | 0.0936          | 0.9684   |
| 0.0003        | 2.23  | 3200 | 0.0932          | 0.9684   |
| 0.0001        | 2.3   | 3300 | 0.1247          | 0.9713   |
| 0.0004        | 2.37  | 3400 | 0.0897          | 0.9741   |
| 0.0001        | 2.44  | 3500 | 0.0853          | 0.9784   |
| 0.0002        | 2.51  | 3600 | 0.0948          | 0.9770   |
| 0.0002        | 2.58  | 3700 | 0.0957          | 0.9770   |
| 0.0043        | 2.65  | 3800 | 0.0868          | 0.9756   |
| 0.0001        | 2.72  | 3900 | 0.0904          | 0.9741   |
| 0.0011        | 2.79  | 4000 | 0.0881          | 0.9770   |
| 0.0001        | 2.86  | 4100 | 0.0890          | 0.9784   |
| 0.0001        | 2.92  | 4200 | 0.0896          | 0.9784   |
| 0.0001        | 2.99  | 4300 | 0.0894          | 0.9784   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2