---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: rmsProp_VitB-p16-384-2e-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: 0.985632183908046
---

<!-- 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. -->

# rmsProp_VitB-p16-384-2e-4-batch_16_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0544
- Accuracy: 0.9856

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.0503        | 0.07  | 100  | 3.0273          | 0.0934   |
| 2.2522        | 0.14  | 200  | 2.4833          | 0.2328   |
| 1.5093        | 0.21  | 300  | 1.3361          | 0.5503   |
| 1.0645        | 0.28  | 400  | 1.0976          | 0.6580   |
| 0.5308        | 0.35  | 500  | 0.5680          | 0.8161   |
| 0.3545        | 0.42  | 600  | 0.3870          | 0.8664   |
| 0.2051        | 0.49  | 700  | 0.3348          | 0.9023   |
| 0.2241        | 0.56  | 800  | 0.1545          | 0.9411   |
| 0.2165        | 0.63  | 900  | 0.1722          | 0.9569   |
| 0.1589        | 0.7   | 1000 | 0.1554          | 0.9497   |
| 0.0647        | 0.77  | 1100 | 0.1400          | 0.9483   |
| 0.1178        | 0.84  | 1200 | 0.2000          | 0.9411   |
| 0.0518        | 0.91  | 1300 | 0.1856          | 0.9483   |
| 0.0433        | 0.97  | 1400 | 0.1573          | 0.9468   |
| 0.0228        | 1.04  | 1500 | 0.1156          | 0.9626   |
| 0.1261        | 1.11  | 1600 | 0.0628          | 0.9727   |
| 0.001         | 1.18  | 1700 | 0.0730          | 0.9770   |
| 0.0515        | 1.25  | 1800 | 0.1589          | 0.9468   |
| 0.0195        | 1.32  | 1900 | 0.1114          | 0.9641   |
| 0.0696        | 1.39  | 2000 | 0.1507          | 0.9555   |
| 0.0006        | 1.46  | 2100 | 0.0799          | 0.9741   |
| 0.0063        | 1.53  | 2200 | 0.0979          | 0.9684   |
| 0.0337        | 1.6   | 2300 | 0.1191          | 0.9598   |
| 0.0261        | 1.67  | 2400 | 0.0839          | 0.9727   |
| 0.001         | 1.74  | 2500 | 0.0911          | 0.9770   |
| 0.001         | 1.81  | 2600 | 0.0726          | 0.9799   |
| 0.0003        | 1.88  | 2700 | 0.0581          | 0.9842   |
| 0.0004        | 1.95  | 2800 | 0.0544          | 0.9856   |


### Framework versions

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