---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
model-index:
- name: vit-base-patch16-224-in21k-finetuned-mgasior-07-02-2024
  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: 0.7716535433070866
---

<!-- 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-base-patch16-224-in21k-finetuned-mgasior-07-02-2024

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: 0.8842
- F1: 0.7717

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.731         | 0.98  | 35   | 1.6748          | 0.3386 |
| 1.5196        | 1.99  | 71   | 1.4890          | 0.4173 |
| 1.3727        | 2.99  | 107  | 1.2938          | 0.5276 |
| 1.2194        | 4.0   | 143  | 1.1519          | 0.6457 |
| 1.1538        | 4.98  | 178  | 1.0544          | 0.6693 |
| 1.0379        | 5.99  | 214  | 0.9852          | 0.7165 |
| 1.0232        | 6.99  | 250  | 0.9439          | 0.7323 |
| 0.9586        | 8.0   | 286  | 0.9136          | 0.7480 |
| 0.9374        | 8.98  | 321  | 0.8946          | 0.7638 |
| 0.96          | 9.79  | 350  | 0.8842          | 0.7717 |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0