update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: CrackDetectionLowRes
+  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: 0.9940476190476191
+---
+
+<!-- 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. -->
+
+# CrackDetectionLowRes
+
+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:
+- Accuracy: 0.9940
+- Loss: 0.0183
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 1337
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Accuracy | Validation Loss |
+|:-------------:|:-----:|:----:|:--------:|:---------------:|
+| 0.0126        | 1.0   | 992  | 0.9879   | 0.0344          |
+| 0.0788        | 2.0   | 1904 | 0.9933   | 0.0220          |
+| 0.1336        | 3.0   | 2856 | 0.9933   | 0.0222          |
+| 0.0066        | 4.0   | 3808 | 0.9933   | 0.0190          |
+| 0.0528        | 5.0   | 4760 | 0.9940   | 0.0183          |
+
+
+### Framework versions
+
+- Transformers 4.31.0.dev0
+- Pytorch 2.0.1+cpu
+- Datasets 2.13.1
+- Tokenizers 0.13.3