ODAch is a test-time-augmentation tool for pytorch 2d object detectors.
Project description
ODAch, An Object Detection TTA tool for Pytorch
ODA is a test-time-augmentation (TTA) tool for 2d object detectors.
For use in Kaggle object detection competitions.
:star: if it helps you! ;)
Install
pip install odach
Usage
See Example.ipynb
.
The setup is very simple, similar to ttach.
Singlescale TTA
import odach as oda
# Declare TTA variations
tta = [oda.HorizontalFlip(), oda.VerticalFlip(), oda.Rotate90(), oda.Multiply(0.9), oda.Multiply(1.1)]
# load image
img = loadimg(impath)
# wrap model and tta
tta_model = oda.TTAWrapper(model, tta)
# Execute TTA!
boxes, scores, labels = tta_model(img)
Multiscale TTA
import odach as oda
# Declare TTA variations
tta = [oda.HorizontalFlip(), oda.VerticalFlip(), oda.Rotate90(), oda.Multiply(0.9), oda.Multiply(1.1)]
# Declare scales to tta
scale = [0.8, 0.9, 1, 1.1, 1.2]
# load image
img = loadimg(impath)
# wrap model and tta
tta_model = oda.TTAWrapper(model, tta, scale)
# Execute TTA!
boxes, scores, labels = tta_model(img)
-
The boxes are also filtered by nms(wbf default).
-
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
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