cyclical-learning-rates
There are 16 repositories under cyclical-learning-rates topic.
deadskull7/One-Stop-for-COVID-19-Infection-and-Lung-Segmentation-plus-Classification
✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation
psklight/keras_one_cycle_clr
Keras callbacks for one-cycle training, cyclic learning rate (CLR) training, and learning rate range test.
ZohebAbai/Tiny-ImageNet-Challenge
This repository contains the jupyter notebooks for the custom-built DenseNet Model build on Tiny ImageNet dataset
pedroosodrac/Paper-to-Code
Paper to Code automates the incorporation of research paper concepts into practical code using OpenAI's GPT models, bridging theory and implementation.
shubhajitml/food-101
training food-101 (achieved SOTA top-1 validation acc ~=90%) using 1-cycle-policy:
fitushar/Cyclical-Learning-Rates-for-Training-Neural-Networks-With-Unbalanced-Data-Sets
As the learning rate is one of the most important hyper-parameters to tune for training convolutional neural networks. In this paper, a powerful technique to select a range of learning rates for a neural network that named cyclical learning rate was implemented with two different skewness degrees. It is an approach to adjust where the value is cycled between a lower bound and upper bound. CLR policies are computationally simpler and can avoid the computational expense of fine tuning with fixed learning rate. It is clearly shown that changing the learning rate during the training phase provides by far better results than fixed values with similar or even smaller number of epochs.
tisu19021997/cyclical-scheduler
Cyclical Learning Rate and 1Cycle Policy as Keras callback.
ayushdabra/ImageClassificationProject-IITK
This repository contains the Jupyter notebook for the custom-built VGG16 Model build for the Tiny ImageNet dataset.
CNC-IISER-BHOPAL/Tiny-ImageNet-Visual-Recognition-Challenge-IITK
This repository contains the Jupyter notebook for the custom-built VGG16 Model build for the Tiny ImageNet dataset.
coxy1989/clr
Pytorch implementation of the paper: 'Cyclical Learning Rates for Training Neural Networks'
abhijitpal1247/DeepSAT-6-Satellite-Image-Classification
Using the pre-trained ImageNet models and cyclical learning rates, we tried to classify the DeepSAT-6 dataset (https://csc.lsu.edu/~saikat/deepsat/) into 6 categories (barren land, trees, grassland, roads, buildings and water bodies). Due to the absence of occlusion by the cloud, we dropped the NIR channel of the data.
shubhajitml/footware
Classify footware based on closures : https://nbviewer.jupyter.org/github/shubhajitml/footware/tree/master/
SevenZhan/Pytorch
self-used pytorch utilities
imrahulr/Quora-Insincere-Questions
Deep Learning for Insincere Question Classification