---
license: apache-2.0
base_model: google/vit-base-patch32-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: adam_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: 0.985632183908046
---

<!-- 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. -->

# adam_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: 0.0868
- 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0118        | 0.14  | 100  | 0.1744          | 0.9555   |
| 0.0816        | 0.28  | 200  | 0.1820          | 0.9526   |
| 0.0035        | 0.42  | 300  | 0.0829          | 0.9741   |
| 0.0704        | 0.56  | 400  | 0.1208          | 0.9713   |
| 0.0591        | 0.7   | 500  | 0.2084          | 0.9540   |
| 0.0213        | 0.84  | 600  | 0.1119          | 0.9641   |
| 0.0024        | 0.97  | 700  | 0.1958          | 0.9555   |
| 0.005         | 1.11  | 800  | 0.1730          | 0.9583   |
| 0.0007        | 1.25  | 900  | 0.1245          | 0.9684   |
| 0.0052        | 1.39  | 1000 | 0.1296          | 0.9698   |
| 0.0001        | 1.53  | 1100 | 0.1377          | 0.9713   |
| 0.0114        | 1.67  | 1200 | 0.1394          | 0.9727   |
| 0.0016        | 1.81  | 1300 | 0.1606          | 0.9698   |
| 0.0137        | 1.95  | 1400 | 0.0984          | 0.9799   |
| 0.0001        | 2.09  | 1500 | 0.0925          | 0.9856   |
| 0.0005        | 2.23  | 1600 | 0.0882          | 0.9856   |
| 0.0001        | 2.37  | 1700 | 0.0900          | 0.9828   |
| 0.0001        | 2.51  | 1800 | 0.0876          | 0.9813   |
| 0.0001        | 2.65  | 1900 | 0.0943          | 0.9813   |
| 0.0001        | 2.79  | 2000 | 0.0899          | 0.9842   |
| 0.0001        | 2.92  | 2100 | 0.0867          | 0.9842   |
| 0.0006        | 3.06  | 2200 | 0.0873          | 0.9856   |
| 0.0001        | 3.2   | 2300 | 0.0875          | 0.9842   |
| 0.0001        | 3.34  | 2400 | 0.0878          | 0.9842   |
| 0.0001        | 3.48  | 2500 | 0.0879          | 0.9842   |
| 0.0001        | 3.62  | 2600 | 0.0877          | 0.9856   |
| 0.0001        | 3.76  | 2700 | 0.0876          | 0.9856   |
| 0.0           | 3.9   | 2800 | 0.0868          | 0.9856   |


### Framework versions

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