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: S2_M1_R3_vit_42499514
+  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: 0.9974554707379135
+---
+
+<!-- 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. -->
+
+# S2_M1_R3_vit_42499514
+
+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: 0.0101
+- Accuracy: 0.9975
+
+## 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: 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.0599        | 0.99  | 73   | 0.0336          | 0.9983   |
+| 0.0232        | 1.99  | 147  | 0.0114          | 0.9975   |
+| 0.0036        | 3.0   | 221  | 0.0147          | 0.9966   |
+| 0.0027        | 4.0   | 295  | 0.0120          | 0.9975   |
+| 0.002         | 4.95  | 365  | 0.0101          | 0.9975   |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 1.11.0+cu102
+- Datasets 2.16.0
+- Tokenizers 0.15.0

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