Instructions to use RumiaKit/dinov2-base-rawdata-finetuned-SkinDisease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RumiaKit/dinov2-base-rawdata-finetuned-SkinDisease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RumiaKit/dinov2-base-rawdata-finetuned-SkinDisease") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("RumiaKit/dinov2-base-rawdata-finetuned-SkinDisease") model = AutoModelForImageClassification.from_pretrained("RumiaKit/dinov2-base-rawdata-finetuned-SkinDisease", device_map="auto") - Notebooks
- Google Colab
- Kaggle
dinov2-base-rawdata-finetuned-SkinDisease
This model is a fine-tuned version of facebook/dinov2-base on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9447
- Accuracy: 0.7304
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 OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2959 | 1.0 | 106 | 2.0532 | 0.3888 |
| 1.8706 | 2.0 | 212 | 1.6682 | 0.4964 |
| 1.4866 | 3.0 | 318 | 1.4056 | 0.5734 |
| 1.3351 | 4.0 | 424 | 1.3061 | 0.6076 |
| 1.0229 | 5.0 | 530 | 1.1944 | 0.6493 |
| 0.8150 | 6.0 | 636 | 1.0887 | 0.6804 |
| 0.6637 | 7.0 | 742 | 1.0493 | 0.6980 |
| 0.5005 | 8.0 | 848 | 1.0031 | 0.7066 |
| 0.4477 | 9.0 | 954 | 0.9674 | 0.7229 |
| 0.3201 | 10.0 | 1060 | 0.9447 | 0.7304 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for RumiaKit/dinov2-base-rawdata-finetuned-SkinDisease
Base model
facebook/dinov2-baseEvaluation results
- Accuracy on imagefoldertest set self-reported0.730