---
license: apache-2.0
base_model: google/vit-large-patch32-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
model-index:
- name: vit-large-patch32-384
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: F1
      type: f1
      value: 0.9763018966303854
---

<!-- 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. -->

# vit-large-patch32-384

This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0127
- F1: 0.9763

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1312        | 0.99  | 53   | 0.1215          | 0.7860 |
| 0.0831        | 1.99  | 107  | 0.0570          | 0.9350 |
| 0.0441        | 3.0   | 161  | 0.0348          | 0.9475 |
| 0.0423        | 4.0   | 215  | 0.0342          | 0.9186 |
| 0.0249        | 4.99  | 268  | 0.0232          | 0.9594 |
| 0.0168        | 5.99  | 322  | 0.0279          | 0.9414 |
| 0.0098        | 7.0   | 376  | 0.0242          | 0.9460 |
| 0.0133        | 8.0   | 430  | 0.0181          | 0.9637 |
| 0.0156        | 8.99  | 483  | 0.0101          | 0.9804 |
| 0.0114        | 9.86  | 530  | 0.0127          | 0.9763 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1