---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: rmsprop_VitB-p16-224-2e-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: 0.9597701149425287
---

<!-- 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-p16-224-2e-4-batch_16_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2491
- Accuracy: 0.9598

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.235         | 0.07  | 100  | 3.3086          | 0.0445   |
| 2.3747        | 0.14  | 200  | 2.8722          | 0.1825   |
| 0.4827        | 0.21  | 300  | 0.5081          | 0.8491   |
| 0.2845        | 0.28  | 400  | 0.6097          | 0.8218   |
| 0.1748        | 0.35  | 500  | 0.3931          | 0.8980   |
| 0.0725        | 0.42  | 600  | 0.4109          | 0.9009   |
| 0.1573        | 0.49  | 700  | 0.3453          | 0.9138   |
| 0.1495        | 0.56  | 800  | 0.3197          | 0.9152   |
| 0.1026        | 0.63  | 900  | 0.3218          | 0.9181   |
| 0.1645        | 0.7   | 1000 | 0.2377          | 0.9296   |
| 0.1471        | 0.77  | 1100 | 0.2831          | 0.9138   |
| 0.0889        | 0.84  | 1200 | 0.1498          | 0.9598   |
| 0.1578        | 0.91  | 1300 | 0.2784          | 0.9325   |
| 0.0856        | 0.97  | 1400 | 0.2652          | 0.9454   |
| 0.0248        | 1.04  | 1500 | 0.2398          | 0.9440   |
| 0.0027        | 1.11  | 1600 | 0.2065          | 0.9583   |
| 0.0109        | 1.18  | 1700 | 0.3166          | 0.9325   |
| 0.0072        | 1.25  | 1800 | 0.3091          | 0.9368   |
| 0.0708        | 1.32  | 1900 | 0.3574          | 0.9267   |
| 0.1434        | 1.39  | 2000 | 0.3906          | 0.9282   |
| 0.0038        | 1.46  | 2100 | 0.3159          | 0.9325   |
| 0.0588        | 1.53  | 2200 | 0.2547          | 0.9511   |
| 0.0009        | 1.6   | 2300 | 0.2685          | 0.9411   |
| 0.0488        | 1.67  | 2400 | 0.6258          | 0.8966   |
| 0.0079        | 1.74  | 2500 | 0.3972          | 0.9282   |
| 0.1493        | 1.81  | 2600 | 0.2655          | 0.9598   |
| 0.042         | 1.88  | 2700 | 0.2861          | 0.9468   |
| 0.0275        | 1.95  | 2800 | 0.3714          | 0.9382   |
| 0.0404        | 2.02  | 2900 | 0.3931          | 0.9325   |
| 0.0002        | 2.09  | 3000 | 0.3090          | 0.9454   |
| 0.0141        | 2.16  | 3100 | 0.4250          | 0.9195   |
| 0.0067        | 2.23  | 3200 | 0.2866          | 0.9497   |
| 0.0003        | 2.3   | 3300 | 0.2893          | 0.9526   |
| 0.0254        | 2.37  | 3400 | 0.3169          | 0.9483   |
| 0.0003        | 2.44  | 3500 | 0.2359          | 0.9526   |
| 0.0001        | 2.51  | 3600 | 0.2565          | 0.9540   |
| 0.001         | 2.58  | 3700 | 0.3259          | 0.9468   |
| 0.0005        | 2.65  | 3800 | 0.2131          | 0.9598   |
| 0.0013        | 2.72  | 3900 | 0.2858          | 0.9526   |
| 0.0014        | 2.79  | 4000 | 0.2378          | 0.9598   |
| 0.0076        | 2.86  | 4100 | 0.2497          | 0.9598   |
| 0.0244        | 2.92  | 4200 | 0.2342          | 0.9583   |
| 0.0002        | 2.99  | 4300 | 0.2881          | 0.9598   |
| 0.0           | 3.06  | 4400 | 0.2758          | 0.9555   |
| 0.0011        | 3.13  | 4500 | 0.2810          | 0.9555   |
| 0.0007        | 3.2   | 4600 | 0.2978          | 0.9598   |
| 0.0           | 3.27  | 4700 | 0.2581          | 0.9626   |
| 0.0           | 3.34  | 4800 | 0.2640          | 0.9641   |
| 0.0           | 3.41  | 4900 | 0.2531          | 0.9598   |
| 0.013         | 3.48  | 5000 | 0.2582          | 0.9626   |
| 0.0004        | 3.55  | 5100 | 0.2815          | 0.9598   |
| 0.0           | 3.62  | 5200 | 0.2768          | 0.9598   |
| 0.0           | 3.69  | 5300 | 0.2803          | 0.9612   |
| 0.0           | 3.76  | 5400 | 0.2619          | 0.9612   |
| 0.0           | 3.83  | 5500 | 0.2594          | 0.9612   |
| 0.0204        | 3.9   | 5600 | 0.2473          | 0.9612   |
| 0.0           | 3.97  | 5700 | 0.2491          | 0.9598   |


### Framework versions

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