VINBIGDATA - FUSING BBOXES + BUILDING YOLO DATASET
\n\n\n","metadata":{}},{"cell_type":"markdown","source":"Overview\n\nThis notebook explores the different ways to select or fuse multiple bboxes of the same chest abnormality, annotated by several radiologists
\n\nIt also covers the conversion of custom bbox annotations to the YoLo format for frameworks such as YOLO etc
\n\n\n**A few key intricacies in this competetion includes the way the training data is provided. To state a few:** \n\n- **The abnormalities are labelled by multiple radiologists and there seems to be multiple bounding boxes for some abnormalities.**\n\n- **Another issue being that some dense abnormality/lesion area may contain multiple labels. The radiologists creates boxes and then they may assign many labels to a single bounding box. This was stated by one of the competetion hosts.**\n\nSo the challenge of this competetion includes handling these issues before or after model training.Some ways to handle this would be to use suppression, selection or fusion techniques below which are covered in this notebook:\n\n- **Non-maximum Suppression (NMS)**\n- **Soft-NMS**\n- **Non-maximum Weighted (NMW)**\n- **Weighted Bboxes Fusion (WBF)**\n\n\n\nThese are generally used after model scoring to get to a consensus of a bounding box where the abnormality is based on the confidence scores, weightage of different models (if ensembling is used) etc.\n\nHere, the challenge lies in the fact that we don't have confidence scores or metrics to assign weightage to the multiple annotations by radiologists. So all radiologists are treated equally and the suppression or fusion of bounding boxes has to be done with these factors out of the picture. This seems to show a behaviour by which the bounding boxes which appear alone, seems to get suppressed. This issue is handled in this notebook by separating out single bounding boxes before any technique is applied.\n\n**Finally, please feel free to suggest any other novel methods to address these issues. Hope everyone finds this useful!**\n\n\n\nCheck out the training notebook which uses the Yolo dataset generated here. It explains the Installation, Data preparation, Training and Inference using the Yolov8 model l. It can be adapted to various other models with just 1-2 lines of code change.
\n\n\nvincxr-yolov8l⚡📈
\n\n\nTRAINING NOTEBOOK - https://www.kaggle.com/code/buithanhxuan/vincxr-yolov8l\n\nThe Yolo dataset with Fused Boxes generated as a part of this notebook is public. Please do check it out.
\n\n\nVinBigData - Yolo Dataset with WBF 3x Downscaled
\n\n\nDATASET LINK - https://www.kaggle.com/datasets/buithanhxuan/vinbigdata-yolo-dataset-with-wbf-3x-downscaled\n\n**Please note that All images with Chest Abnormalities are present in this dataset.**\n\nTHANK YOU! PLEASE UPVOTE
\n\nHOPE IT WAS USEFUL
\n\nCheck out the Train Notebook below
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