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-1e-4-15ep
+  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: 0.8867063492063492
+---
+
+<!-- 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-1e-4-15ep
+
+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: 0.3897
+- Accuracy: 0.8867
+
+## 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: 0.0001
+- train_batch_size: 64
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.5399        | 1.0   | 275  | 0.4756          | 0.8676   |
+| 0.2126        | 2.0   | 550  | 0.4134          | 0.8875   |
+| 0.0726        | 3.0   | 825  | 0.4687          | 0.8775   |
+| 0.0345        | 4.0   | 1100 | 0.4552          | 0.8883   |
+| 0.0123        | 5.0   | 1375 | 0.5129          | 0.8851   |
+| 0.0068        | 6.0   | 1650 | 0.4877          | 0.8954   |
+| 0.0063        | 7.0   | 1925 | 0.4667          | 0.9018   |
+| 0.0055        | 8.0   | 2200 | 0.4697          | 0.9030   |
+| 0.0021        | 9.0   | 2475 | 0.4620          | 0.9054   |
+| 0.0039        | 10.0  | 2750 | 0.4652          | 0.9058   |
+| 0.0027        | 11.0  | 3025 | 0.4658          | 0.9058   |
+| 0.0024        | 12.0  | 3300 | 0.4668          | 0.9078   |
+| 0.0021        | 13.0  | 3575 | 0.4671          | 0.9078   |
+| 0.0019        | 14.0  | 3850 | 0.4681          | 0.9062   |
+| 0.002         | 15.0  | 4125 | 0.4682          | 0.9062   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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