---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: rmsProp_vitB-32-384-2e4-ne-1-bs-16
  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: 0.9827586206896551
---

<!-- 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-32-384-2e4-ne-1-bs-16

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0752
- Accuracy: 0.9828

## 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.774         | 0.07  | 100  | 3.4867          | 0.0819   |
| 1.1104        | 0.14  | 200  | 1.5104          | 0.5359   |
| 0.2994        | 0.21  | 300  | 0.3914          | 0.8836   |
| 0.2754        | 0.28  | 400  | 0.2456          | 0.9210   |
| 0.212         | 0.35  | 500  | 0.1669          | 0.9511   |
| 0.1063        | 0.42  | 600  | 0.1406          | 0.9511   |
| 0.0999        | 0.49  | 700  | 0.2218          | 0.9425   |
| 0.0609        | 0.56  | 800  | 0.1407          | 0.9598   |
| 0.0784        | 0.63  | 900  | 0.0985          | 0.9641   |
| 0.0251        | 0.7   | 1000 | 0.0963          | 0.9698   |
| 0.0094        | 0.77  | 1100 | 0.0893          | 0.9727   |
| 0.0153        | 0.84  | 1200 | 0.1044          | 0.9670   |
| 0.032         | 0.91  | 1300 | 0.1035          | 0.9713   |
| 0.0042        | 0.97  | 1400 | 0.0752          | 0.9828   |


### Framework versions

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