Test-Time-Augmentation is a very efficient way to improve the results of any model at testing time.
-
Updated
May 21, 2020 - Jupyter Notebook
Test-Time-Augmentation is a very efficient way to improve the results of any model at testing time.
Object Detection and Bounding Box Prediction using YOLO5 and EfficientDet , Image Augmentations and Test Time Augmentations
My modified version of EfficientDet training, cross-validation and inference with Pseudo Labelling pytorch pipelines used in GWD Kaggle Competition
My modified version of YoloV5 training, cross-validation and inference with Pseudo Labelling pytorch pipelines used in GWD Kaggle Competition
Image recognition used to distinguish between bees and wasps in photographs
Simple but high-performing method for learning a policy of test-time augmentation
Test Time Augmentation for Deep Learning Inference
Image Test Time Augmentation with PyTorch!
Wheat detection using Faster RCNN
Test time augmentation with Tensorflow keras models for segmentation tasks. This package also enables creation of keras layers for GPU acceleration
Test-Time Augmentation library for Pytorch
Code Implementation for EmbedSeg, an Instance Segmentation Method for Microscopy Images
Engage in a semantic segmentation challenge for land cover description using multimodal remote sensing earth observation data, delving into real-world scenarios with a dataset comprising 70,000+ aerial imagery patches and 50,000 Sentinel-2 satellite acquisitions.
PyTorch extensions for fast R&D prototyping and Kaggle farming
Collection of awesome test-time (domain/batch/instance) adaptation methods
Add a description, image, and links to the test-time-augmentation topic page so that developers can more easily learn about it.
To associate your repository with the test-time-augmentation topic, visit your repo's landing page and select "manage topics."