---
license: apache-2.0
base_model: google/vit-base-patch32-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: rmsProp_ViTB-32-224-in21k-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: 0.9367816091954023
---

<!-- 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-32-224-in21k-2e-4-batch_16_epoch_4_classes_24

This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3224
- Accuracy: 0.9368

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7852        | 0.07  | 100  | 2.7614          | 0.1494   |
| 1.9058        | 0.14  | 200  | 1.8155          | 0.4813   |
| 1.1158        | 0.21  | 300  | 1.0835          | 0.7328   |
| 0.8073        | 0.28  | 400  | 0.7663          | 0.7931   |
| 0.4979        | 0.35  | 500  | 0.5854          | 0.8376   |
| 0.3927        | 0.42  | 600  | 0.3968          | 0.8865   |
| 0.3211        | 0.49  | 700  | 0.4845          | 0.8621   |
| 0.2881        | 0.56  | 800  | 0.3788          | 0.8865   |
| 0.233         | 0.63  | 900  | 0.4301          | 0.8649   |
| 0.2446        | 0.7   | 1000 | 0.3978          | 0.8836   |
| 0.351         | 0.77  | 1100 | 0.4557          | 0.8649   |
| 0.1268        | 0.84  | 1200 | 0.3445          | 0.8908   |
| 0.1973        | 0.91  | 1300 | 0.5640          | 0.8477   |
| 0.1321        | 0.97  | 1400 | 0.4515          | 0.8793   |
| 0.0914        | 1.04  | 1500 | 0.3113          | 0.9109   |
| 0.1177        | 1.11  | 1600 | 0.4890          | 0.8664   |
| 0.0532        | 1.18  | 1700 | 0.4442          | 0.8836   |
| 0.1037        | 1.25  | 1800 | 0.3393          | 0.9210   |
| 0.0857        | 1.32  | 1900 | 0.4536          | 0.8865   |
| 0.1201        | 1.39  | 2000 | 0.4316          | 0.8937   |
| 0.162         | 1.46  | 2100 | 0.3895          | 0.9023   |
| 0.0761        | 1.53  | 2200 | 0.3556          | 0.9239   |
| 0.054         | 1.6   | 2300 | 0.3811          | 0.9052   |
| 0.1666        | 1.67  | 2400 | 0.3660          | 0.9195   |
| 0.1038        | 1.74  | 2500 | 0.4206          | 0.9066   |
| 0.068         | 1.81  | 2600 | 0.4614          | 0.8980   |
| 0.0452        | 1.88  | 2700 | 0.4233          | 0.9037   |
| 0.0395        | 1.95  | 2800 | 0.4487          | 0.8994   |
| 0.0064        | 2.02  | 2900 | 0.4042          | 0.9124   |
| 0.006         | 2.09  | 3000 | 0.3274          | 0.9095   |
| 0.0153        | 2.16  | 3100 | 0.4322          | 0.8951   |
| 0.0674        | 2.23  | 3200 | 0.3815          | 0.9052   |
| 0.052         | 2.3   | 3300 | 0.5513          | 0.8966   |
| 0.0058        | 2.37  | 3400 | 0.4124          | 0.9181   |
| 0.0331        | 2.44  | 3500 | 0.2915          | 0.9339   |
| 0.0014        | 2.51  | 3600 | 0.3652          | 0.9224   |
| 0.0485        | 2.58  | 3700 | 0.3655          | 0.9181   |
| 0.0463        | 2.65  | 3800 | 0.4681          | 0.9066   |
| 0.0008        | 2.72  | 3900 | 0.3798          | 0.9224   |
| 0.0007        | 2.79  | 4000 | 0.3576          | 0.9239   |
| 0.0008        | 2.86  | 4100 | 0.3683          | 0.9239   |
| 0.0062        | 2.92  | 4200 | 0.3980          | 0.9210   |
| 0.0009        | 2.99  | 4300 | 0.3483          | 0.9253   |
| 0.0006        | 3.06  | 4400 | 0.3098          | 0.9382   |
| 0.0003        | 3.13  | 4500 | 0.3137          | 0.9339   |
| 0.0003        | 3.2   | 4600 | 0.3562          | 0.9325   |
| 0.0692        | 3.27  | 4700 | 0.3462          | 0.9296   |
| 0.0215        | 3.34  | 4800 | 0.3913          | 0.9239   |
| 0.0549        | 3.41  | 4900 | 0.3144          | 0.9397   |
| 0.0004        | 3.48  | 5000 | 0.3545          | 0.9368   |
| 0.0008        | 3.55  | 5100 | 0.3295          | 0.9397   |
| 0.0002        | 3.62  | 5200 | 0.3135          | 0.9382   |
| 0.0004        | 3.69  | 5300 | 0.3249          | 0.9368   |
| 0.0003        | 3.76  | 5400 | 0.3157          | 0.9353   |
| 0.0002        | 3.83  | 5500 | 0.3242          | 0.9368   |
| 0.0002        | 3.9   | 5600 | 0.3222          | 0.9353   |
| 0.0002        | 3.97  | 5700 | 0.3224          | 0.9368   |


### Framework versions

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