---
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-chest_xray
  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: 0.978
    - name: F1
      type: f1
      value: 0.9779992079714871
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# dinov2-Base-finetuned-chest_xray

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: 0.1155
- Accuracy: 0.978
- F1: 0.9780

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6168        | 1.0   | 500  | 0.3097          | 0.881    | 0.8804 |
| 0.4064        | 2.0   | 1000 | 0.2299          | 0.931    | 0.9309 |
| 0.2011        | 3.0   | 1500 | 0.1904          | 0.943    | 0.9430 |
| 0.148         | 4.0   | 2000 | 0.2213          | 0.94     | 0.9399 |
| 0.2495        | 5.0   | 2500 | 0.2518          | 0.933    | 0.9328 |
| 0.1926        | 6.0   | 3000 | 0.1155          | 0.966    | 0.9660 |
| 0.1565        | 7.0   | 3500 | 0.1711          | 0.959    | 0.9590 |
| 0.1881        | 8.0   | 4000 | 0.1235          | 0.967    | 0.9670 |
| 0.139         | 9.0   | 4500 | 0.1285          | 0.97     | 0.9700 |
| 0.1317        | 10.0  | 5000 | 0.1155          | 0.978    | 0.9780 |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0