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-classifier
+  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: 0.7313780260707635
+---
+
+<!-- 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-classifier
+
+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: 0.5720
+- Accuracy: 0.7314
+
+## 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-06
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.2
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.646         | 1.0   | 537   | 0.6400          | 0.6420   |
+| 0.5941        | 2.0   | 1074  | 0.5874          | 0.6974   |
+| 0.5259        | 3.0   | 1611  | 0.5849          | 0.7142   |
+| 0.5459        | 4.0   | 2148  | 0.5645          | 0.7197   |
+| 0.5086        | 5.0   | 2685  | 0.5554          | 0.7230   |
+| 0.5397        | 6.0   | 3222  | 0.5540          | 0.7295   |
+| 0.5646        | 7.0   | 3759  | 0.5491          | 0.7272   |
+| 0.4564        | 8.0   | 4296  | 0.5771          | 0.7235   |
+| 0.4951        | 9.0   | 4833  | 0.5518          | 0.7267   |
+| 0.5074        | 10.0  | 5370  | 0.5556          | 0.7300   |
+| 0.5512        | 11.0  | 5907  | 0.5739          | 0.7165   |
+| 0.5003        | 12.0  | 6444  | 0.5648          | 0.7235   |
+| 0.4442        | 13.0  | 6981  | 0.5581          | 0.7230   |
+| 0.4787        | 14.0  | 7518  | 0.5556          | 0.7402   |
+| 0.4944        | 15.0  | 8055  | 0.5589          | 0.7342   |
+| 0.4678        | 16.0  | 8592  | 0.5567          | 0.7379   |
+| 0.5569        | 17.0  | 9129  | 0.5601          | 0.7314   |
+| 0.4164        | 18.0  | 9666  | 0.5619          | 0.7365   |
+| 0.4406        | 19.0  | 10203 | 0.5711          | 0.7309   |
+| 0.453         | 20.0  | 10740 | 0.5720          | 0.7314   |
+
+
+### Framework versions
+
+- Transformers 4.39.0.dev0
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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