Tejasri Nampally's repositories
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 .
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
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
Fastai2-Medium
Repository with the code (notebooks) and data (training patches) for the medium stories covering Fastai 2.
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"
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!
show-control-and-tell
Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions. CVPR 2019
techniques
Techniques for deep learning with satellite & aerial imagery
Train-Test-Validation-Dataset-Generation
This repository contains the files for running the Patchify GUI.