---
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: dinov2-base-finetuned-dermnet-lr3-5-0.05wd-csr
  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: 0.7958001448225923
---

<!-- 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-dermnet-lr3-5-0.05wd-csr

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: 0.8119
- Accuracy: 0.7958

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.7488        | 1.0     | 98   | 2.5158          | 0.3722   |
| 1.9402        | 2.0     | 196  | 1.7710          | 0.5170   |
| 1.4938        | 3.0     | 294  | 1.4939          | 0.5996   |
| 1.1226        | 4.0     | 392  | 1.3168          | 0.6256   |
| 0.9329        | 5.0     | 490  | 1.1906          | 0.6705   |
| 0.8039        | 6.0     | 588  | 1.0882          | 0.7067   |
| 0.6426        | 7.0     | 686  | 1.1061          | 0.6930   |
| 0.5777        | 8.0     | 784  | 1.0133          | 0.7227   |
| 0.477         | 9.0     | 882  | 0.9681          | 0.7364   |
| 0.3961        | 10.0    | 980  | 0.9402          | 0.7581   |
| 0.3451        | 11.0    | 1078 | 0.9311          | 0.7509   |
| 0.337         | 12.0    | 1176 | 0.8897          | 0.7661   |
| 0.2348        | 13.0    | 1274 | 0.8616          | 0.7762   |
| 0.1992        | 14.0    | 1372 | 0.8241          | 0.7951   |
| 0.182         | 15.0    | 1470 | 0.8312          | 0.7878   |
| 0.1556        | 16.0    | 1568 | 0.8245          | 0.7857   |
| 0.1516        | 17.0    | 1666 | 0.8170          | 0.7958   |
| 0.1569        | 18.0    | 1764 | 0.8202          | 0.7878   |
| 0.1364        | 19.0    | 1862 | 0.8117          | 0.7951   |
| 0.1427        | 19.8021 | 1940 | 0.8119          | 0.7958   |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1