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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

basal cell carcinoma darier_s disease epidermolysis bullosa pruriginosa hailey-hailey disease herpes simplex impetigo larva migrans leprosy borderline leprosy lepromatous leprosy tuberculoid lichen planus lupus erythematosus chronicus discoides melanoma molluscum contagiosum mycosis fungoides neurofibromatosis papilomatosis confluentes and reticulate pediculosis capitis pityriasis rosea porokeratosis actinic psoriasis tinea corporis tinea nigra tungiasis unknown vitiligo actinic keratosis dermatofibroma nevus seborrheic keratosis squamous cell carcinoma vascular lesion