Emiel
/

cub-200-bird-classifier-swin

@@ -4,6 +4,7 @@ metrics:
 - accuracy
 base_model:
 - microsoft/swin-large-patch4-window12-384-in22k
 ---
 # Model Card for Model ID
@@ -21,7 +22,8 @@ base_model:
 <!-- Provide a longer summary of what this model is. -->
 This model was created for the "Feather in Focus!" Kaggle competition of the Information Studies master: Applied Machine Learning course at the University of Amsterdam.
-The goal of the competition was to apply novel approaches to reach the highest possible accuracy on a bird classification task with 200 classes.
 - **Model type:** [More Information Needed]
 - **License:** [More Information Needed]
@@ -32,9 +34,7 @@ The goal of the competition was to apply novel approaches to reach the highest p
 ### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ### Training Procedure
@@ -76,79 +76,3 @@ The goal of the competition was to apply novel approaches to reach the highest p
 #### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 - accuracy
 base_model:
 - microsoft/swin-large-patch4-window12-384-in22k
+license: apache-2.0
 ---
 # Model Card for Model ID
 <!-- Provide a longer summary of what this model is. -->
 This model was created for the "Feather in Focus!" Kaggle competition of the Information Studies master: Applied Machine Learning course at the University of Amsterdam.
+The goal of the competition was to apply novel approaches to reach the highest possible accuracy on a bird classification task with 200 classes. We were given a labeled dataset
+of 3926 images and an unlabeled dataset of 4000 test images.
 - **Model type:** [More Information Needed]
 - **License:** [More Information Needed]
 ### Training Data
+The training data consists of an unkown subset of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011
 ### Training Procedure
 #### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->