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Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation,classification, super-resolution, ... etc.

Project description


Edafa is a simple wrapper that implements Test Time Augmentations (TTA) on images for computer vision problems like: segmentation, classification, super-resolution, Pansharpening, etc. TTAs guarantees better results in most of the tasks.

Test Time Augmentation (TTA)

Applying different transformations to test images and then average for more robust results.



pip install edafa

Getting started

The easiest way to get up and running is to follow example notebooks for segmentation and classification showing TTA effect on performance.

How to use Edafa

The whole process can be done in 4 steps:

  1. Import Predictor class based on your task category: Segmentation (SegPredictor) or Classification (ClassPredictor)
from edafa import SegPredictor
  1. Inherit Predictor class and implement the main function
    • predict_patches(self,patches) : where your model takes image patches (numpy.ndarray) and return prediction (numpy.ndarray)
class myPredictor(SegPredictor):
    def __init__(self,model,*args,**kwargs):
        self.model = model

    def predict_patches(self,patches):
        return self.model.predict(patches)
  1. Create an instance of you class
p = myPredictor(model,patch_size,model_output_channels,conf_file_path)
  1. Call predict_images() to run the prediction process

Configuration file

Configuration file is a json file containing two pieces of information

  1. Augmentations to apply (augs). Supported augmentations:
    • NO : No augmentation
    • ROT90 : Rotate 90 degrees
    • ROT180 : Rotate 180 degrees
    • ROT270 : Rotate 270 degrees
    • FLIP_UD : Flip upside-down
    • FLIP_LR : Flip left-right
  2. Combination of the results (mean). Supported mean types:
    • ARITH : Arithmetic mean
    • GEO : Geometric mean

Example of a conf file


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