Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-patch16-224-RU2-10
+  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: 0.8166666666666667
+---
+
+<!-- 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. -->
+
+# vit-base-patch16-224-RU2-10
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6802
+- Accuracy: 0.8167
+
+## 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: 5.5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.1641        | 0.99  | 38   | 0.9789          | 0.7333   |
+| 0.5847        | 2.0   | 77   | 0.6371          | 0.8167   |
+| 0.2844        | 2.99  | 115  | 0.6706          | 0.75     |
+| 0.2275        | 4.0   | 154  | 0.5359          | 0.8167   |
+| 0.1539        | 4.99  | 192  | 0.6067          | 0.8167   |
+| 0.1113        | 6.0   | 231  | 0.7887          | 0.7667   |
+| 0.1117        | 6.99  | 269  | 0.6443          | 0.8167   |
+| 0.1088        | 8.0   | 308  | 0.6429          | 0.85     |
+| 0.0824        | 8.99  | 346  | 0.6499          | 0.8333   |
+| 0.0834        | 9.87  | 380  | 0.6802          | 0.8167   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.2+cu118
+- Datasets 2.16.1
+- Tokenizers 0.15.0

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