---
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: ky-finetuned-skindiseasefinal
  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: 0.9552567237163814
---

<!-- 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. -->

# ky-finetuned-skindiseasefinal

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: 0.1301
- Accuracy: 0.9553

## 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: Use 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.4949        | 0.9974 | 287  | 0.6912          | 0.7770   |
| 0.7268        | 1.9974 | 574  | 0.3927          | 0.8775   |
| 0.5179        | 2.9974 | 861  | 0.3185          | 0.8983   |
| 0.4193        | 3.9974 | 1148 | 0.2439          | 0.9191   |
| 0.3576        | 4.9974 | 1435 | 0.2107          | 0.9301   |
| 0.3015        | 5.9974 | 1722 | 0.1821          | 0.9386   |
| 0.2648        | 6.9974 | 2009 | 0.1685          | 0.9411   |
| 0.2228        | 7.9974 | 2296 | 0.1497          | 0.9487   |
| 0.1946        | 8.9974 | 2583 | 0.1407          | 0.9494   |
| 0.1625        | 9.9974 | 2870 | 0.1301          | 0.9553   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0