Joyeeta Dey's starred repositories

darts

Differentiable architecture search for convolutional and recurrent networks

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Hyperspectral_Image_Analysis_Simplified

The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:220Issues:2Issues:3

FairDARTS

Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search

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CNN_Enhanced_GCN

Q. Liu, L. Xiao, J. Yang and Z. Wei, "CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3037361.

nasbot

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Language:PythonLicense:MITStargazers:132Issues:11Issues:8

iNAS

Open Source Neural Architecture Search Toolbox for Device-aware Image Dense Prediction & Official implementation of ICCV2021 "iNAS: Integral NAS for Device-Aware Salient Object Detection"

Language:PythonLicense:NOASSERTIONStargazers:84Issues:2Issues:3

Auto-CNN-HSI-Classification

Code for the paper "Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification"

Language:Jupyter NotebookLicense:BSD-3-ClauseStargazers:40Issues:7Issues:0

A-Fast-and-Compact-3-D-CNN-for-HSIC

The code is associated with the following paper "A Fast and Compact 3-D CNN for Hyperspectral Image Classification". IEEE Geoscience and Remote Sensing Letters

Language:Jupyter NotebookStargazers:35Issues:1Issues:2

HC_ADGAN

Code for the paper "Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification", IEEE TGRS 2021

rnn_darts_fastai

Implement Differentiable Architecture Search (DARTS) for RNN with fastai

Language:Jupyter NotebookStargazers:24Issues:2Issues:1

HyT-NAS

The code for “Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification”

SpecPatConv3D-Network

An implementation of the neural network described in "Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification"

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sharpDARTS

sharpDARTS: Faster and More Accurate Differentiable Architecture Search

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Cross-domain-CNN

Cross Domain CNN for Hyperspectral Image Classification

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DWDM-CSRGFF

This repo is the implementation of the Cascade superpixel regularized Gabor Feature Fusion For hyperspectral image classification as a part of the Data Warehousing and Data Mining course mini project

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hyperspectral_images_classification

Hyperspectral images have more spectral information, which can be used to classify surfaces/crops in remote sensing applications. The same has been explored in this repository.

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swarm-torch

Optimizer for PyTorch that enables to train any PyTorch model without gradients using Particle Swarm Optimization.

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pc-darts

Clean PC-DARTS implementation in PytTorch

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Thai-Nutrition-Table-Extraction

The nutrition table extractor from Thai food packages. Mini project in Computer Vision class.

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Feature_selection_HSI_classification_AP_disease

This repository contains my master's thesis under the title of "Feature selection in hyperspectral images for classification of apple proliferation disease"

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NAS-HIC-Blog

Blog on Neural Architecture Search For Hand Image Classification

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FreeDARTS

codes for Differentiable Architecture Search Meets Network Pruning at Initialization: A More Reliable, Efficient, and Flexible Framework

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Masking-Spectral-Crop-Data

Hyperspectral imaging (HSI) is a technique that analyzes a wide spectrum of light instead of just assigning primary colors (red, green, blue) to each pixel. HSI is used across many applications within the field of biosystems engineering, including agricultural production, food processing, microbial analysis, and more. Given its high spectral resolution, HSI can provide unique signatures that enable the detection and quantification of biological, chemical, and physical phenomena.

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ENAS-PyTorch

PyTorch implementation of ENAS

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Ingredients-Extraction

This project helps in extracting the ingredients mentioned in the food packets with OpenCV and Natural Language Processing

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OptDARTS

Codes for CSED490Y Project: Optimizer Combination Analysis for Differentiable Neural Architecture Search

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