update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- nthu-ddd
+metrics:
+- accuracy
+model-index:
+- name: vit-base-nthu-ddd
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: nthu-ddd
+      type: nthu-ddd
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9595640736565201
+---
+
+<!-- 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-nthu-ddd
+
+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 nthu-ddd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1214
+- Accuracy: 0.9596
+
+## 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: 0.001
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.4811        | 0.6   | 2000  | 0.5214          | 0.7636   |
+| 0.3339        | 1.2   | 4000  | 0.3437          | 0.8621   |
+| 0.284         | 1.8   | 6000  | 0.2679          | 0.8932   |
+| 0.2143        | 2.41  | 8000  | 0.2269          | 0.9125   |
+| 0.0997        | 3.01  | 10000 | 0.1576          | 0.9444   |
+| 0.1168        | 3.61  | 12000 | 0.1214          | 0.9596   |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 1.13.1+cu116
+- Datasets 2.11.0
+- Tokenizers 0.13.2