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adamatch-pytorch

A PyTorch implementation of AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

agricultural-price-prediction-and-visualization-on-android-app

In Agriculture Price Monitioring , I have used data provided by open government site data.gov.in, which updates prices of market daily . Working Interface Details: We have provided user choice to see current market prices based on two choices: market wise or commodity wise use increase assesibility options. Market wise: User have to provide State,District and Market name and then select market wise button. Then user will be shown the prices of all the commodities present in the market in graphical format, so that he can analyse the rates on one scale. This feature is mostly helpful for a regular buyer to decide the choice of commodity to buy. He is also given feature to download the data in a tabular format(csv) for accurate analysis. Commodity Wise: User have to provide State,District and Commodity name and then select Commodity wise button. Then user will be shown the prices of all the markets present in the region with the commodity in graphical format, so that he can analyse the cheapest commodity rate. This feature is mostly helpful for wholesale buyers. He is also given feature to download the data in a tabular format(csv) for accurate analysis. On the first activity user is also given forecasting choice. It can be used to forecast the wholesale prices of various commodities at some later year. Regression techniques on timeseries data is used to predict future prices. Select the type of item and click link for future predictions. There are 3 java files Forecasts, DisplayGraphs, DisplayGraphs2 ..... Please change the localhost "server_name" at time of testing as the server name changes each time a new server is made. Things Used: We have used pandas , numpy , scikit learn , seaborn and matplotlib libraries for the same . The dataset is thoroughly analysed using different function available in pandas in my .iPynb file . Not just in-built functions are used but also many user made functions are made to make the working smooth . Various graphs like pointplot , heat-map , barplot , kdeplot , distplot, pairplot , stripplot , jointplot, regplot , etc are made and also deployed on the android app as well . To integrate the android app and machine learning analysis outputs , we have used Flask to host our laptop as the server . We have a separate file for the Flask as server.py . Where all the the necessary stuff of clint request and server response have been dealt with . We have used npm package ngrok for tunneling purpose and hosting . A different .iPynb file is used for the time series predictions using regression algorithms and would send the csv file of prediction along with the graph to the andoid app when given a request .

airquality_analysis_dublin

best-practices

CODIUM's best practices for development

cclabeler

A web tool for labeling pedestrians in an image, provideing two types of label: box and point.

coco-annotator

:pencil2: Web-based image segmentation tool for object detection, localization, and keypoints

crowd_annotation

Annotating human heads in collected images for crowd counting and generating corresponding ground truth .mat files using MATLAB

csrnet-keras

Implementation of the CSRNet paper (CVPR 18) in keras-tensorflow

data-version-control

deepsorghumhead

A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

dronecrowd

Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network

etci-2021-competition-on-flood-detection

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

eurosat_data_analysis

fastai2-medium

Repository with the code (notebooks) and data (training patches) for the medium stories covering Fastai 2.

finetuning-sam

firefox_b2b_comm_radio_addon

Firefox Browser to Browser Radio communication for Internet access

linkedin-skill-assessments-quizzes

Full reference of LinkedIn answers 2022 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers

msmatch

Code for the paper "MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels"

paddy-growth-stage-recognition

panicle-detection

pixelpick

pytorch-retinanet

Pytorch implementation of RetinaNet object detection.

remote-sensing-chatgpt

Chat with RS-ChatGPT and get the remote sensing interpretation results and the response!

sebastinsanty.github.io

show-control-and-tell

Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions. CVPR 2019

stressnet

techniques

Techniques for deep learning with satellite & aerial imagery

tejasri19

Config files for my GitHub profile.

train-test-validation-dataset-generation

This repository contains the files for running the Patchify GUI.