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-ethosrealdata
+  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: 0.934010152284264
+---
+
+<!-- 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-ethosrealdata
+
+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: 0.2117
+- Accuracy: 0.9340
+
+## 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: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.9707        | 0.9913 | 57   | 0.6825          | 0.8160   |
+| 0.3507        | 2.0    | 115  | 0.3680          | 0.8909   |
+| 0.2002        | 2.9913 | 172  | 0.3121          | 0.9023   |
+| 0.1249        | 4.0    | 230  | 0.2951          | 0.9150   |
+| 0.1002        | 4.9913 | 287  | 0.2596          | 0.9251   |
+| 0.1014        | 6.0    | 345  | 0.2615          | 0.9251   |
+| 0.1261        | 6.9913 | 402  | 0.2437          | 0.9365   |
+| 0.0556        | 8.0    | 460  | 0.2198          | 0.9416   |
+| 0.0415        | 8.9913 | 517  | 0.2119          | 0.9416   |
+| 0.0294        | 9.9130 | 570  | 0.2117          | 0.9340   |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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