Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: skin_decease
+  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: 0.9871794871794872
+---
+
+<!-- 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. -->
+
+# skin_decease
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0680
+- Accuracy: 0.9872
+
+## 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: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 8
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.2359        | 0.8621 | 100  | 0.2427          | 0.9744   |
+| 0.086         | 1.7241 | 200  | 0.1178          | 0.9872   |
+| 0.0435        | 2.5862 | 300  | 0.0801          | 0.9872   |
+| 0.0312        | 3.4483 | 400  | 0.0748          | 0.9872   |
+| 0.023         | 4.3103 | 500  | 0.0715          | 0.9872   |
+| 0.0197        | 5.1724 | 600  | 0.0696          | 0.9872   |
+| 0.0174        | 6.0345 | 700  | 0.0687          | 0.9872   |
+| 0.0161        | 6.8966 | 800  | 0.0684          | 0.9872   |
+| 0.0151        | 7.7586 | 900  | 0.0680          | 0.9872   |
+
+
+### Framework versions
+
+- Transformers 4.43.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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