---
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-1e-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: 0.9583333333333334
---

<!-- 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-1e-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: 0.1773
- Accuracy: 0.9583

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5163        | 0.07  | 100  | 1.4460          | 0.8779   |
| 0.7184        | 0.14  | 200  | 0.8418          | 0.8764   |
| 0.3902        | 0.21  | 300  | 0.5001          | 0.9066   |
| 0.3509        | 0.28  | 400  | 0.4132          | 0.9138   |
| 0.1873        | 0.35  | 500  | 0.3207          | 0.9282   |
| 0.2124        | 0.42  | 600  | 0.3325          | 0.9239   |
| 0.1122        | 0.49  | 700  | 0.2961          | 0.9267   |
| 0.135         | 0.56  | 800  | 0.2646          | 0.9353   |
| 0.1275        | 0.63  | 900  | 0.2384          | 0.9411   |
| 0.1194        | 0.7   | 1000 | 0.2189          | 0.9397   |
| 0.1968        | 0.77  | 1100 | 0.2539          | 0.9282   |
| 0.078         | 0.84  | 1200 | 0.2293          | 0.9353   |
| 0.0345        | 0.91  | 1300 | 0.1585          | 0.9626   |
| 0.1068        | 0.97  | 1400 | 0.2896          | 0.9181   |
| 0.0429        | 1.04  | 1500 | 0.1876          | 0.9497   |
| 0.0153        | 1.11  | 1600 | 0.2637          | 0.9296   |
| 0.0296        | 1.18  | 1700 | 0.2153          | 0.9454   |
| 0.0262        | 1.25  | 1800 | 0.2906          | 0.9339   |
| 0.023         | 1.32  | 1900 | 0.2271          | 0.9483   |
| 0.0154        | 1.39  | 2000 | 0.2772          | 0.9397   |
| 0.0778        | 1.46  | 2100 | 0.2510          | 0.9353   |
| 0.0504        | 1.53  | 2200 | 0.1866          | 0.9555   |
| 0.0563        | 1.6   | 2300 | 0.2228          | 0.9454   |
| 0.0481        | 1.67  | 2400 | 0.2390          | 0.9497   |
| 0.0602        | 1.74  | 2500 | 0.2601          | 0.9382   |
| 0.0253        | 1.81  | 2600 | 0.2905          | 0.9339   |
| 0.0433        | 1.88  | 2700 | 0.2805          | 0.9325   |
| 0.0039        | 1.95  | 2800 | 0.2264          | 0.9425   |
| 0.0476        | 2.02  | 2900 | 0.2195          | 0.9511   |
| 0.0036        | 2.09  | 3000 | 0.2174          | 0.9511   |
| 0.003         | 2.16  | 3100 | 0.1697          | 0.9583   |
| 0.0026        | 2.23  | 3200 | 0.2194          | 0.9555   |
| 0.0049        | 2.3   | 3300 | 0.2416          | 0.9468   |
| 0.0023        | 2.37  | 3400 | 0.1703          | 0.9612   |
| 0.0023        | 2.44  | 3500 | 0.2082          | 0.9583   |
| 0.0262        | 2.51  | 3600 | 0.2328          | 0.9468   |
| 0.0026        | 2.58  | 3700 | 0.1798          | 0.9583   |
| 0.0458        | 2.65  | 3800 | 0.2406          | 0.9483   |
| 0.0027        | 2.72  | 3900 | 0.2298          | 0.9511   |
| 0.0019        | 2.79  | 4000 | 0.1836          | 0.9598   |
| 0.0146        | 2.86  | 4100 | 0.2025          | 0.9526   |
| 0.0739        | 2.92  | 4200 | 0.1946          | 0.9598   |
| 0.0054        | 2.99  | 4300 | 0.1972          | 0.9569   |
| 0.0015        | 3.06  | 4400 | 0.2329          | 0.9511   |
| 0.0015        | 3.13  | 4500 | 0.2094          | 0.9569   |
| 0.0016        | 3.2   | 4600 | 0.1888          | 0.9598   |
| 0.0017        | 3.27  | 4700 | 0.1853          | 0.9569   |
| 0.0014        | 3.34  | 4800 | 0.1809          | 0.9612   |
| 0.0013        | 3.41  | 4900 | 0.1802          | 0.9583   |
| 0.0014        | 3.48  | 5000 | 0.1795          | 0.9569   |
| 0.0012        | 3.55  | 5100 | 0.1737          | 0.9583   |
| 0.0012        | 3.62  | 5200 | 0.1777          | 0.9569   |
| 0.0012        | 3.69  | 5300 | 0.1796          | 0.9555   |
| 0.0012        | 3.76  | 5400 | 0.1782          | 0.9569   |
| 0.0012        | 3.83  | 5500 | 0.1771          | 0.9583   |
| 0.0012        | 3.9   | 5600 | 0.1773          | 0.9583   |
| 0.0011        | 3.97  | 5700 | 0.1773          | 0.9583   |


### Framework versions

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