---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned-bin
  results: []
---

<!-- 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. -->

# finetuned-bin

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7526
- Accuracy: 0.0582
- F1: 0.0356

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.7759        | 0.13  | 100  | 2.7526          | 0.0582   | 0.0356 |
| 2.7726        | 0.25  | 200  | 2.7794          | 0.0627   | 0.0208 |
| 2.7741        | 0.38  | 300  | 2.7754          | 0.0853   | 0.0450 |
| 2.775         | 0.5   | 400  | 2.7739          | 0.0818   | 0.0357 |
| 2.7738        | 0.63  | 500  | 2.7691          | 0.1204   | 0.0434 |
| 2.7737        | 0.75  | 600  | 2.7744          | 0.0573   | 0.0349 |
| 2.7732        | 0.88  | 700  | 2.7759          | 0.0484   | 0.0244 |
| 2.7746        | 1.0   | 800  | 2.7592          | 0.0942   | 0.0434 |
| 2.7737        | 1.13  | 900  | 2.7727          | 0.1098   | 0.0541 |
| 2.7715        | 1.25  | 1000 | 2.7719          | 0.0893   | 0.0414 |
| 2.7742        | 1.38  | 1100 | 2.7985          | 0.0      | 0.0    |
| 2.7715        | 1.51  | 1200 | 2.7729          | 0.024    | 0.0158 |
| 2.7698        | 1.63  | 1300 | 2.7711          | 0.0649   | 0.0344 |
| 2.7717        | 1.76  | 1400 | 2.7709          | 0.0858   | 0.0387 |
| 2.7708        | 1.88  | 1500 | 2.7726          | 0.0587   | 0.0368 |
| 2.7736        | 2.01  | 1600 | 2.8029          | 0.0      | 0.0    |
| 2.7726        | 2.13  | 1700 | 2.7743          | 0.088    | 0.0327 |
| 2.7734        | 2.26  | 1800 | 2.7734          | 0.0284   | 0.0163 |
| 2.7726        | 2.38  | 1900 | 2.7731          | 0.0578   | 0.0330 |
| 2.7733        | 2.51  | 2000 | 2.7711          | 0.1098   | 0.0498 |
| 2.7729        | 2.63  | 2100 | 2.7769          | 0.0018   | 0.0019 |
| 2.773         | 2.76  | 2200 | 2.7631          | 0.1076   | 0.0319 |
| 2.7743        | 2.89  | 2300 | 2.7768          | 0.0      | 0.0    |
| 2.7736        | 3.01  | 2400 | 2.7883          | 0.0      | 0.0    |
| 2.7726        | 3.14  | 2500 | 2.7727          | 0.104    | 0.0495 |
| 2.7727        | 3.26  | 2600 | 2.7694          | 0.0969   | 0.0569 |
| 2.7733        | 3.39  | 2700 | 2.7861          | 0.0004   | 0.0008 |
| 2.7739        | 3.51  | 2800 | 2.7727          | 0.1022   | 0.0575 |
| 2.7734        | 3.64  | 2900 | 2.7728          | 0.112    | 0.0478 |
| 2.7732        | 3.76  | 3000 | 2.7729          | 0.1236   | 0.0567 |
| 2.7736        | 3.89  | 3100 | 2.7727          | 0.1218   | 0.0587 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2