PyTorch extensions for fast R&D prototyping and Kaggle farming

Image Test Time Augmentation with PyTorch!

Collection of awesome test-time (domain/batch/instance) adaptation methods

Code Implementation for EmbedSeg, an Instance Segmentation Method for Microscopy Images

Simple but high-performing method for learning a policy of test-time augmentation

My modified version of YoloV5 training, cross-validation and inference with Pseudo Labelling pytorch pipelines used in GWD Kaggle Competition

Wheat detection using Faster RCNN

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.

My modified version of EfficientDet training, cross-validation and inference with Pseudo Labelling pytorch pipelines used in GWD Kaggle Competition

Source code for the CIKM 2024 paper "Post-Training Embedding Enhancement for Long-Tail Recommendation."

Object Detection and Bounding Box Prediction using YOLO5 and EfficientDet , Image Augmentations and Test Time Augmentations

Test-Time Augmentation library for Pytorch

Image recognition used to distinguish between bees and wasps in photographs

Test time augmentation with Tensorflow keras models for segmentation tasks. This package also enables creation of keras layers for GPU acceleration

Test Time Augmentation for Deep Learning Inference

Test-Time-Augmentation is a very efficient way to improve the results of any model at testing time.