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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: 0.3367 Accuracy: 0.8950
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Source description
This model is a fine-tuned version of facebook/dinov2-base on the imagefolder dataset. It achieves the following results on the evaluation set:
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1 sourceVerified Aug 5
Model artifacts
1 artifactSource excerpts
3 excerptsMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.3032 | 1.0 | 27 | 1.6952 | 0.4761 |
| 1.2438 | 2.0 | 54 | 1.0713 | 0.6528 |
| 0.8899 | 3.0 | 81 | 0.8296 | 0.7266 |
| 0.6481 | 4.0 | 108 | 0.6101 | 0.7973 |
| 0.4851 | 5.0 | 135 | 0.5342 | 0.8295 |
--- library_name: transformers license: apache-2.0 base_model: facebook/dinov2-base tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: dino.atlas_isic-finetuned-SkinDisease results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.895010395010395 --- <!-- 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. --> # dino.atlas_isic-finetuned-SkinDisease 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: 0.3367 - Accuracy: 0.8950 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.3032 | 1.0 | 27 | 1.6952 | 0.4761 | | 1.2438 | 2.0 | 54 | 1.0713 | 0.6528 | | 0.8899 | 3.0 | 81 | 0.8296 | 0.7266 | | 0.6481 | 4.0 | 108 | 0.6101 | 0.7973 | | 0.4851 | 5.0 | 135 | 0.5342 | 0.8295 | | 0.3545 | 6.0 | 162 | 0.5102 | 0.8399 | | 0.2937 | 7.0 | 189 | 0.4151 | 0.8638 | | 0.2350 | 8.0 | 216 | 0.3534 | 0.8950 | | 0.1488 | 9.0 | 243 | 0.3433 | 0.8888 | | 0.1454 | 10.0 | 270 | 0.3367 | 0.8950 | ### Framework versions - Transformers 5.5.4 - Pytorch 2.10.0+cu128 - Datasets 4.8.4 - Tokenizers 0.22.2
Source context: 13 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo aidenshindel/dino.atlas_isic-finetuned-SkinDisease
Source context: 12 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo aidenshindel/dino.atlas_isic-finetuned-SkinDisease
| 0.3545 | 6.0 | 162 | 0.5102 | 0.8399 |
| 0.2937 | 7.0 | 189 | 0.4151 | 0.8638 |
| 0.2350 | 8.0 | 216 | 0.3534 | 0.8950 |
| 0.1488 | 9.0 | 243 | 0.3433 | 0.8888 |
| 0.1454 | 10.0 | 270 | 0.3367 | 0.8950 |
