---
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: dinov2-base-finetuned-eye
  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: 0.968
    - name: F1
      type: f1
      value: 0.9678344915175675
---

<!-- 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. -->

# dinov2-base-finetuned-eye

This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2262
- Accuracy: 0.968
- F1: 0.9678

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3853        | 1.0   | 250  | 0.4918          | 0.874    | 0.8729 |
| 0.5345        | 2.0   | 500  | 0.4390          | 0.878    | 0.8771 |
| 0.4693        | 3.0   | 750  | 0.3857          | 0.88     | 0.8796 |
| 0.1933        | 4.0   | 1000 | 0.3444          | 0.894    | 0.8948 |
| 0.3146        | 5.0   | 1250 | 0.2456          | 0.936    | 0.9362 |
| 0.1832        | 6.0   | 1500 | 0.3369          | 0.924    | 0.9229 |
| 0.1407        | 7.0   | 1750 | 0.3425          | 0.946    | 0.9454 |
| 0.1462        | 8.0   | 2000 | 0.2864          | 0.948    | 0.9476 |
| 0.0905        | 9.0   | 2250 | 0.2177          | 0.956    | 0.9560 |
| 0.0859        | 10.0  | 2500 | 0.2262          | 0.968    | 0.9678 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0