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--- |
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library_name: transformers |
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metrics: |
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- accuracy |
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base_model: |
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- microsoft/swin-large-patch4-window12-384-in22k |
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license: apache-2.0 |
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tags: |
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- vision |
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- image-classification |
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- vt |
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model-index: |
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- name: cub-200-bird-classifier-swin |
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results: |
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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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name: cub-200-2011 |
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type: cub-200-2011 |
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args: default |
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metrics: |
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- type: accuracy |
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value: 0.8653 |
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name: validation_accuracy |
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- type: accuracy |
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value: 0.8795 |
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name: test_accuracy |
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--- |
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# Model Card for Model ID |
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### Model Description |
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This model was finetuned for the "Feather in Focus!" Kaggle competition of the Information Studies Master's Applied Machine Learning course at the University of Amsterdam. |
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The goal of the competition was to apply novel approaches to achieve the highest possible accuracy on a bird classification task with 200 classes. |
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We were given a labeled dataset of 3926 images and an unlabeled dataset of 4000 test images. |
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Out of 32 groups and 1083 submissions, we achieved the #1 accuracy on the test set with a score of 0.87950. |
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### Training Details |
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The model we are finetuning, microsoft/swin-large-patch4-window12-384-in22k, was pre-trained on imagenet-21k, see https://huggingface.co./microsoft/swin-large-patch4-window12-384-in22k. |
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#### Preprocessing |
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Data augmentation was applied to the training data in a custom Torch dataset class. Because of the size of the dataset, images were not replaced but were duplicated and augmented. |
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The only augmentations applied were HorizontalFlips and Rotations (10 degrees) to align with the relatively homogeneous dataset. |
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# Finetuning |
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Finetuning was done on some 50 different models including different VTs and CNNs. All models were trained for 10 epochs with the best model, based on the evaluation acccuracy, |
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saved every epoch. |
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### Finetuning Data |
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The finetuning data is a subset of the cub-200-2011 dataset, http://www.vision.caltech.edu/datasets/cub_200_2011/. |
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We finetuned the model on 3533 samples of the labeled dataset we were given, stratified on the label (7066 including augmented images). |
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#### Finetuning Hyperparameters |
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| Hyperparameter | Value | |
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|-----------------------|----------------------------| |
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| Optimizer | AdamW | |
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| Learning Rate | 1e-4 | |
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| Batch Size | 32 | |
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| Epochs | 2 | |
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| Weight Decay | * | |
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| Class Weight | * | |
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| Label Smoothing | * | |
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| Scheduler | * | |
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| Mixed Precision | Torch AMP | |
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*parameters were intentionally not set because of poor results |
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### Evaluation Data |
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The evaluation data is a subset of the cub-200-2011 dataset, http://www.vision.caltech.edu/datasets/cub_200_2011/. |
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We evaluated the model on 393 samples of the labeled dataset we were given, stratified on the label. |
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#### Testing Data |
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The testing data is a subset of an unlabeled subset of the cub-200-2011 dataset, http://www.vision.caltech.edu/datasets/cub_200_2011/ of 4000 images. After model finetuning |
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the best model, based on the evaluation data, would be loaded. This model would then be used to predict the labels of the unlabeled test set. |
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These predicted labels were submitted to the Kaggle competition via CSV which returned the test accuracy. |
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### Poster |
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*novel approaches were not applied when finetuning the final model as they did not improve accuracy. |
