update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: vit_base_aihub_model_py
+  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: 0.9985872380503885
+    - name: Precision
+      type: precision
+      value: 0.9989954885489135
+    - name: Recall
+      type: recall
+      value: 0.998161142953993
+    - name: F1
+      type: f1
+      value: 0.9985770990024514
+---
+
+<!-- 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_aihub_model_py
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0217
+- Accuracy: 0.9986
+- Precision: 0.9990
+- Recall: 0.9982
+- F1: 0.9986
+
+## 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: 128
+- eval_batch_size: 128
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 512
+- 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 | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.1235        | 1.0   | 149  | 0.0936          | 0.9858   | 0.9845    | 0.9814 | 0.9830 |
+| 0.067         | 2.0   | 299  | 0.0622          | 0.9878   | 0.9909    | 0.9813 | 0.9859 |
+| 0.049         | 3.0   | 448  | 0.0322          | 0.9968   | 0.9969    | 0.9959 | 0.9964 |
+| 0.0477        | 4.0   | 598  | 0.0249          | 0.9978   | 0.9985    | 0.9965 | 0.9975 |
+| 0.0336        | 4.98  | 745  | 0.0217          | 0.9986   | 0.9990    | 0.9982 | 0.9986 |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.12.0
+- Tokenizers 0.13.3