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license: mit |
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task_categories: |
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- object-detection |
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- image-classification |
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tags: |
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- medical |
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--- |
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Face Masks ensemble dataset is no longer limited to [Kaggle](https://www.kaggle.com/datasets/henrylydecker/face-masks), it is now coming to Huggingface! |
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This dataset was created to help train and/or fine tune models for detecting masked and un-masked faces. |
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I created a new face masks object detection dataset by compositing together three publically available face masks object detection datasets on Kaggle that used the YOLO annotation format. |
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To combine the datasets, I used Roboflow. |
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All three original datasets had different class dictionaries, so I recoded the classes into two classes: "Mask" and "No Mask". |
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One dataset included a class for incorrectly worn face masks, images with this class were removed from the dataset. |
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Approximately 50 images had corrupted annotations, so they were manually re-annotated in the Roboflow platform. |
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The final dataset includes 9,982 images, with 24,975 annotated instances. |
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Image resolution was on average 0.49 mp, with a median size of 750 x 600 pixels. |
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To improve model performance on out of sample data, I used 90 degree rotational augmentation. |
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This saved duplicate versions of each image for 90, 180, and 270 degree rotations. |
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I then split the data into 85% training, 10% validation, and 5% testing. |
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Images with classes that were removed from the dataset were removed, leaving 16,000 images in training, 1,900 in validation, and 1,000 in testing. |
