Update README.md

README.md

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