Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- accelerator
+metrics:
+- accuracy
+model-index:
+- name: finetuned-vit-base-patch16-224-upside-down-detector
+  results: []
+---
+
+# finetuned-vit-base-patch16-224-upside-down-detector
+
+This model is a fine-tuned version of [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom image orientation dataset adapted from the [beans](https://huggingface.co/datasets/beans) dataset. It achieves the following results on the evaluation set:
+- Accuracy: 0.8947
+
+## Training and evaluation data
+
+The custom dataset for image orientation adapted from [beans](https://huggingface.co/datasets/beans) dataset contains a total of 2,590 image samples with 1,295 original and 1,295 upside down. The model was fine-tuned on the train subset and evaluated on validation and test subsets. The dataset splits are listed below:
+
+| Split      | # examples |
+|:----------:|:----------:|
+| train      | 2068       |
+| validation | 133        |
+| test       | 128        |
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-04
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 32
+- num_epochs: 5
+
+### Training results
+
+| Epoch      | Accuracy   |
+|:----------:|:----------:|
+| 0          | 0.8609     |
+| 1          | 0.8835     |
+| 2          | 0.8571     |
+| 3          | 0.8941     |
+| 4          | 0.8941     |
+
+### Framework versions
+
+- Transformers 4.17.0
+- Pytorch 1.9.0+cu111
+- Pytorch/XLA 1.9
+- Datasets 2.0.0
+- Tokenizers 0.12.0