PyTorch extensions for fast R&D prototyping and Kaggle farming
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Updated
Nov 21, 2024 - Python
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
This project explores workplace safety compliance in construction sites by evaluating a Convolutional Neural Network (CNN) for binary classification of images into "safe" (individuals wearing safety equipment) and "unsafe" (individuals not wearing safety equipment).
Test-Time-Augmentation is a very efficient way to improve the results of any model at testing time.
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