Dinov2 Base Finetuned SkinDisease

--- library_name: transformers license: apache-2.0 base_model: facebook/dinov2-base tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall model-index: - name: dinov2-base-finetuned-SkinDisease results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.8739 - name: Precision type: precision value: 0.8857 - name: Recall type: recall value: 0.8674 --- <!-- 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-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: 1.0419 - Accuracy: 0.8739 - Precision: 0.8857 - Recall: 0.8674 - F1 Macro: 0.8739 - F1 Weighted: 0.873 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Macro | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:-----------:| | 4.4724 | 1.0 | 83 | 1.5323 | 0.6862 | 0.7616 | 0.7242 | 0.6951 | 0.6781 | | 3.4639 | 2.0 | 166 | 1.2999 | 0.7933 | 0.8467 | 0.8179 | 0.8146 | 0.7985 | | 3.1461 | 3.0 | 249 | 1.1596 | 0.824 | 0.8302 | 0.8288 | 0.8207 | 0.8216 | | 2.9459 | 4.0 | 332 | 1.1939 | 0.8372 | 0.8607 | 0.8284 | 0.834 | 0.8292 | | 2.8444 | 5.0 | 415 | 1.1071 | 0.8548 | 0.8714 | 0.8574 | 0.8595 | 0.8563 | | 2....