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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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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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name: Image Classification |
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type: image-classification |
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dataset: |
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name: cub-200-subset |
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type: cub-200-subset |
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args: default |
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metrics: |
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- name: validation_accuracy |
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type: accuracy |
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value: 0.86530 |
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- name: test_accuracy |
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type: accuracy |
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value: 0.87950 |
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--- |
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# Model Card for Model ID |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model was created 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 3,926 images and an unlabeled dataset of 4,000 test images. |
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Out of 32 groups and 1,083 submissions, we achieved the #1 accuracy on the test set with a score of 0.87950. |
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- **Model type:** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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## Training Details |
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### Training Data |
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The training data consists of an unknown subset of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011 |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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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 duplicated and augmented. |
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The only augmentations applied were HorizontalFlips and Rotations (10 degrees) to align with the relatively homogenous dataset. |
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#### Training 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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| Scheduler | - | |
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| Mixed Precision | Torch AMP | |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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The testing data consists of an unknown subset of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011 |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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