Metadata-Version: 2.1
Name: odach
Version: 0.1.0-2010290533
Summary: ODAch is a test-time-augmentation tool for pytorch 2d object detectors.
Home-page: https://github.com/kentaroy47/ODA-Object-Detection-ttA
Author: Kentaro Yoshioka
Author-email: meathouse47@gmail.com
License: MIT
Description: # ODA-Object-Detection-ttA
        ODA is a test-time-augmentation tool for 2d object detectors. 
        
        Used in Kaggle object detection competitions!
        
        # Install
        `pip install oda`
        
        # Usage
        See Example.ipynb.
        
        The setup is very simple, similar to ttach.
        
        ```
        import oda
        # Declare single scale TTA variations
        mono = [oda.VerticalFlip(),oda.HorizontalFlip(), oda.Rotate90(), oda.Multiply(0.9), oda.Multiply(1.1)]
        # Declare multiscale-TTA with 0.8~1.2x image sizes.
        multi = [oda.MultiScale(i) for i in [0.8, 0.9, 1.1, 1.2]] + [oda.MultiScaleFlip(i) for i in [0.8, 0.9, 1.1, 1.2]]
        
        # load image
        impath = "imgs/cars3.jpg"
        img = loadimg(impath)
        # wrap model and tta
        tta_model = oda.TTAWrapper(model, mono, multi)
        # Execute TTA!
        boxes, scores, labels = tta_model.inference(img)
        ```
        
        * The image size should be square.
        
        ## model output wrapping
        * Wrap your detection model so that the output is similar to torchvision frcnn format:
        [["box":[[x,y,x2,y2], [], ..], "labels": [0,1,..], "scores": [1.0, 0.8, ..]]
        
        ## Thanks
        nms, wbf are from https://kaggle.com/zfturbo
        
        tta is based on https://github.com/qubvel/ttach, https://github.com/andrewekhalel/edafa/tree/master/edafa and https://www.kaggle.com/shonenkov/wbf-over-tta-single-model-efficientdet
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
