Model save

README.md

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+---
+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
+should probably proofread and complete it, then remove this comment. -->
+
+# 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