---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Rashed-vit-model
  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: 1.0
---

<!-- 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. -->

# Rashed-vit-model

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0047
- Accuracy: 1.0

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2279        | 1.9048  | 200  | 0.4485          | 0.9111   |
| 0.1335        | 3.8095  | 400  | 0.0680          | 0.9889   |
| 0.0061        | 5.7143  | 600  | 0.0047          | 1.0      |
| 0.0025        | 7.6190  | 800  | 0.0606          | 0.9778   |
| 0.0624        | 9.5238  | 1000 | 0.2500          | 0.9556   |
| 0.0013        | 11.4286 | 1200 | 0.0868          | 0.9889   |
| 0.001         | 13.3333 | 1400 | 0.0908          | 0.9889   |
| 0.0008        | 15.2381 | 1600 | 0.0935          | 0.9889   |
| 0.0006        | 17.1429 | 1800 | 0.0960          | 0.9889   |
| 0.0005        | 19.0476 | 2000 | 0.0979          | 0.9889   |
| 0.0004        | 20.9524 | 2200 | 0.0996          | 0.9889   |
| 0.0004        | 22.8571 | 2400 | 0.1013          | 0.9889   |
| 0.0003        | 24.7619 | 2600 | 0.1027          | 0.9889   |
| 0.0003        | 26.6667 | 2800 | 0.1040          | 0.9889   |
| 0.0003        | 28.5714 | 3000 | 0.1054          | 0.9889   |
| 0.0002        | 30.4762 | 3200 | 0.1065          | 0.9889   |
| 0.0002        | 32.3810 | 3400 | 0.1076          | 0.9889   |
| 0.0002        | 34.2857 | 3600 | 0.1085          | 0.9889   |
| 0.0002        | 36.1905 | 3800 | 0.1094          | 0.9889   |
| 0.0002        | 38.0952 | 4000 | 0.1102          | 0.9889   |
| 0.0002        | 40.0    | 4200 | 0.1109          | 0.9889   |
| 0.0001        | 41.9048 | 4400 | 0.1115          | 0.9889   |
| 0.0001        | 43.8095 | 4600 | 0.1120          | 0.9889   |
| 0.0001        | 45.7143 | 4800 | 0.1124          | 0.9889   |
| 0.0001        | 47.6190 | 5000 | 0.1126          | 0.9889   |
| 0.0001        | 49.5238 | 5200 | 0.1128          | 0.9889   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1