---
license: apache-2.0
base_model: google/vit-base-patch32-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: rmsProps_VitB-p32-384-1e-4-batch_32_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: 0.9770114942528736
---

<!-- 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. -->

# rmsProps_VitB-p32-384-1e-4-batch_32_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co./google/vit-base-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0970
- Accuracy: 0.9770

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0292        | 0.14  | 100  | 0.1481          | 0.9655   |
| 0.0026        | 0.28  | 200  | 0.1295          | 0.9741   |
| 0.043         | 0.42  | 300  | 0.1905          | 0.9641   |
| 0.0099        | 0.56  | 400  | 0.2070          | 0.9641   |
| 0.0028        | 0.7   | 500  | 0.1291          | 0.9727   |
| 0.0206        | 0.84  | 600  | 0.1674          | 0.9612   |
| 0.0007        | 0.97  | 700  | 0.1307          | 0.9684   |
| 0.0004        | 1.11  | 800  | 0.1403          | 0.9698   |
| 0.0564        | 1.25  | 900  | 0.1523          | 0.9670   |
| 0.0247        | 1.39  | 1000 | 0.2492          | 0.9397   |
| 0.0005        | 1.53  | 1100 | 0.1087          | 0.9799   |
| 0.0172        | 1.67  | 1200 | 0.1220          | 0.9741   |
| 0.0073        | 1.81  | 1300 | 0.1126          | 0.9770   |
| 0.0009        | 1.95  | 1400 | 0.1326          | 0.9727   |
| 0.0101        | 2.09  | 1500 | 0.1339          | 0.9713   |
| 0.0018        | 2.23  | 1600 | 0.1344          | 0.9698   |
| 0.0002        | 2.37  | 1700 | 0.1588          | 0.9713   |
| 0.0147        | 2.51  | 1800 | 0.1543          | 0.9698   |
| 0.0292        | 2.65  | 1900 | 0.1266          | 0.9770   |
| 0.0001        | 2.79  | 2000 | 0.1535          | 0.9727   |
| 0.0           | 2.92  | 2100 | 0.1384          | 0.9756   |
| 0.0023        | 3.06  | 2200 | 0.1438          | 0.9713   |
| 0.0001        | 3.2   | 2300 | 0.1258          | 0.9741   |
| 0.0           | 3.34  | 2400 | 0.1038          | 0.9770   |
| 0.0001        | 3.48  | 2500 | 0.1010          | 0.9756   |
| 0.0001        | 3.62  | 2600 | 0.1007          | 0.9770   |
| 0.0           | 3.76  | 2700 | 0.1002          | 0.9770   |
| 0.0           | 3.9   | 2800 | 0.0970          | 0.9770   |


### Framework versions

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