Collection of awesome test-time (domain/batch/instance) adaptation methods
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Updated
May 20, 2024
Collection of awesome test-time (domain/batch/instance) adaptation methods
PyTorch extensions for fast R&D prototyping and Kaggle farming
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.
Code Implementation for EmbedSeg, an Instance Segmentation Method for Microscopy Images
Test-Time Augmentation library for Pytorch
Test time augmentation with Tensorflow keras models for segmentation tasks. This package also enables creation of keras layers for GPU acceleration
Wheat detection using Faster RCNN
Image Test Time Augmentation with PyTorch!
Test Time Augmentation for Deep Learning Inference
Simple but high-performing method for learning a policy of test-time augmentation
Image recognition used to distinguish between bees and wasps in photographs
My modified version of YoloV5 training, cross-validation and inference with Pseudo Labelling pytorch pipelines used in GWD Kaggle Competition
My modified version of EfficientDet training, cross-validation and inference with Pseudo Labelling pytorch pipelines used in GWD Kaggle Competition
Object Detection and Bounding Box Prediction using YOLO5 and EfficientDet , Image Augmentations and Test Time Augmentations
Test-Time-Augmentation is a very efficient way to improve the results of any model at testing time.
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