---
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-skindiseaseicthuawei32
  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: 0.9622508792497069
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ky-finetuned-skindiseaseicthuawei32

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: 0.1058
- Accuracy: 0.9623

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3894        | 1.0   | 300  | 0.6160          | 0.8061   |
| 0.6543        | 2.0   | 600  | 0.4378          | 0.8635   |
| 0.471         | 3.0   | 900  | 0.2566          | 0.9161   |
| 0.3853        | 4.0   | 1200 | 0.2498          | 0.9135   |
| 0.3225        | 5.0   | 1500 | 0.2157          | 0.9290   |
| 0.2769        | 6.0   | 1800 | 0.1747          | 0.9407   |
| 0.2364        | 7.0   | 2100 | 0.1502          | 0.9487   |
| 0.2005        | 8.0   | 2400 | 0.1282          | 0.9547   |
| 0.1737        | 9.0   | 2700 | 0.1129          | 0.9597   |
| 0.1468        | 10.0  | 3000 | 0.1058          | 0.9623   |


### Framework versions

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