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--- |
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license: apache-2.0 |
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tags: |
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- accelerator |
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metrics: |
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- accuracy |
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model-index: |
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- name: finetuned-vit-base-patch16-224-upside-down-detector |
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results: [] |
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widget: |
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- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/original.jpg |
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example_title: original |
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- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/upside_down.jpg |
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example_title: upside_down |
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--- |
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# finetuned-vit-base-patch16-224-upside-down-detector |
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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: |
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- Accuracy: 0.8947 |
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## Training and evaluation data |
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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: |
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| Split | # examples | |
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|:----------:|:----------:| |
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| train | 2068 | |
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| validation | 133 | |
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| test | 128 | |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-04 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 32 |
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- num_epochs: 5 |
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### Training results |
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| Epoch | Accuracy | |
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|:----------:|:----------:| |
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| 0 | 0.8609 | |
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| 1 | 0.8835 | |
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| 2 | 0.8571 | |
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| 3 | 0.8941 | |
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| 4 | 0.8941 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.9.0+cu111 |
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- Pytorch/XLA 1.9 |
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- Datasets 2.0.0 |
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- Tokenizers 0.12.0 |
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