Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- generated_from_trainer
+datasets:
+- renovation
+metrics:
+- accuracy
+model-index:
+- name: vit-base-renovation2
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: renovation
+      type: renovation
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6529680365296804
+---
+
+<!-- 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-renovation2
+
+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 renovation dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6878
+- Accuracy: 0.6530
+
+## 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: 0.0002
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.273         | 0.2   | 25   | 1.2384          | 0.6027   |
+| 0.5153        | 0.4   | 50   | 1.4060          | 0.5845   |
+| 0.2792        | 0.6   | 75   | 1.3026          | 0.5936   |
+| 0.5516        | 0.81  | 100  | 1.3999          | 0.6027   |
+| 0.4247        | 1.01  | 125  | 1.2621          | 0.5982   |
+| 0.1556        | 1.21  | 150  | 1.5661          | 0.5571   |
+| 0.1458        | 1.41  | 175  | 1.3459          | 0.6347   |
+| 0.1595        | 1.61  | 200  | 1.5278          | 0.5982   |
+| 0.1195        | 1.81  | 225  | 1.5303          | 0.6256   |
+| 0.1507        | 2.02  | 250  | 1.7701          | 0.5845   |
+| 0.023         | 2.22  | 275  | 1.5354          | 0.6301   |
+| 0.028         | 2.42  | 300  | 1.6535          | 0.6301   |
+| 0.0698        | 2.62  | 325  | 1.6772          | 0.6438   |
+| 0.0516        | 2.82  | 350  | 1.4380          | 0.6804   |
+| 0.0136        | 3.02  | 375  | 1.6561          | 0.6484   |
+| 0.0325        | 3.23  | 400  | 1.6028          | 0.6621   |
+| 0.0149        | 3.43  | 425  | 1.6261          | 0.6621   |
+| 0.0082        | 3.63  | 450  | 1.6615          | 0.6621   |
+| 0.0093        | 3.83  | 475  | 1.6878          | 0.6530   |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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