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-batch-32
+  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: 0.8755952380952381
+---
+
+<!-- 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-batch-32
+
+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: 0.6201
+- Accuracy: 0.8756
+
+## 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: 0.0003
+- train_batch_size: 32
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.6735        | 1.0   | 550  | 0.8003          | 0.7583   |
+| 0.4048        | 2.0   | 1100 | 0.6471          | 0.8266   |
+| 0.2506        | 3.0   | 1650 | 0.6220          | 0.8354   |
+| 0.1521        | 4.0   | 2200 | 0.6406          | 0.8493   |
+| 0.0812        | 5.0   | 2750 | 0.6855          | 0.8545   |
+| 0.0279        | 6.0   | 3300 | 0.6767          | 0.8648   |
+| 0.0094        | 7.0   | 3850 | 0.6252          | 0.8744   |
+| 0.0074        | 8.0   | 4400 | 0.6064          | 0.8751   |
+| 0.0056        | 9.0   | 4950 | 0.5997          | 0.8783   |
+| 0.0016        | 10.0  | 5500 | 0.6009          | 0.8767   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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