Nitinguptadu

Faster-R-CNN-for-Open-Images-Dataset-by-Keras

Introduction The original code of Keras version of Faster R-CNN I used was written by yhenon (resource link: GitHub .) He used the PASCAL VOC 2007, 2012, and MS COCO datasets. For me, I just extracted three classes, “Person”, “Car” and “Mobile phone”, from Google’s Open Images Dataset V4. I applied configs different from his work to fit my dataset and I removed unuseful code. Btw, to run this on Google Colab (for free GPU computing up to 12hrs), I compressed all the code into three .ipynb notebooks. Sorry for the messy structure. I wrote my exploring and experiment results for Faster R-CNN in this article in Medium. If you are in China, you cannot directly access Medium. So I make a copy in here. Project Structure Object_Detection_DataPreprocessing.ipynb is the file to extract subdata from Open Images Dataset V4 which includes downloading the images and creating the annotation files for our training. I run this part by my own computer because of no need for GPU computation. frcnn_train_vgg.ipynb is the file to train the model. The configuration and model saved path are inside this file. frcnn_test_vgg.ipynb is the file to test the model with test images and calculate the mAP (mean average precision) for the model.

Faster-R-CNN-model-deployment-using-flask-on-local-host

In this Repository I have deployed the faster Rcnn model on flask to deploy it on local server and aslo provided the code for how to use AWS sagamaker to deploy your pre train model on aws

Faster-R-CNN-model-dockerization

ISBI-Challenge-Segmentation-of-neuronal-structures-in-EM-stacks

In this challenge, a full stack of EM slices will be used to train machine learning algorithms for the purpose of automatic segmentation of neural structures. The images are representative of actual images in the real-world, containing some noise and small image alignment errors. None of these problems led to any difficulties in the manual labeling of each element in the image stack by an expert human neuroanatomist. The aim of the challenge is to compare and rank the different competing methods based on their pixel and object classification accuracy.

Multi-label-image-classification-in-keras

Classify the multi-­‐label images classification according to their given label . build the model from the scratch in keras

Object-Detection-using-Fast.ai

The labelled dataset below will have images of damaged cars along with labels for various types of damages. Among various categories of damages, The model only 2 damage categories- scratches or spots, and dents . Use various image enhancement and augmentation techniques to improve accuracy of the model.

Resent-50-from-scratch-for-multi--label-image-classification

kaggle competition iMet Collection 2019 Classify the multi-­‐label images classification according to their given label using a ResNet50 . build the model from the scratch

RSNA-Pneumonia-Detection-Challenge

In this competition, you’re challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, your algorithm needs to automatically locate lung opacities on chest radiographs.

Titanic-Data-set-

The objective was to predict the chances of passenger’s survival Challenges – Dealing with missing value and Data Cleaning Techniques used One Hot encoding for categorical data and Min Max Scaler for numerical data Model used – Logistic Regression, Decision tree (ID3 algorithm), Random Forest Classifier

Yolo-V-3-network-from-scratch-in-pytorch

yolo v3 in pytorch ( python version 3 ) for image real time image detection and video detection (video formate .avi supported by opencv )

Nitinguptadu/Faster-R-CNN-model-dockerization

Nitinguptadu/Gas-sensor-array-under-dynamic-gas-mixtures

Time series data for Ethylene and Methane in air, and Ethylene and CO in air

Nitinguptadu/Nitinguptadu

Breast cancer detection

Nitinguptadu/cat-and-Dog-Nitin

this repo is for self learning purpose

Nitinguptadu/CONV2D-with-Tabular-Data-

This repo is for self learning Purpose

Nitinguptadu/Faster-Rcnn-docker-

Nitinguptadu/Flye

Nitinguptadu/Image-classification-Heroku-

This code is for self learning purpose

Nitinguptadu/Visualizing-Decision-Trees-Random-forest-with-Python

How to Visualize Decision Trees using Matplotlib How to Visualize Decision Trees using Graphviz (what is Graphviz, how to install it on Mac and Windows, and how to use it to visualize decision trees) How to Visualize Individual Decision Trees from Bagged Trees or Random Forests

Nitinguptadu/Ankur-Machine-learning-

Client: M17(https://m17.asia/en/product/17media/) a streaming platform. DataLink: ​ Client_data.zip The attached data here is generated from the live-streaming platform. Analyze the data to come up with the top 20% streamers. Using these top 20% streamers as good streamers create a classification model which can classify whether any streamer is good streamer or not. Evaluation Metric​ : F1-Score Classification Report (Using sklearn.metrics.classification_report) is also required

Nitinguptadu/Covid-19-time-series-Classification

This repo is for self learning purpose

Nitinguptadu/Gas-sensors-for-home-activity-monitoring-Data-Set

Abstract: 100 recordings of a sensor array under different conditions in a home setting: background, wine and banana presentations. The array includes 8 MOX gas sensors, and humidity and temperature sensors. Source: Creators: Flavia Huerta,Gaurav Gawade Ramon Huerta, University of California San Diego, USA Donors: Flavia Huerta Ramon Huerta, University of California San Diego, USA (rhuerta ‘@’ ucsd.edu) Thiago Mosqueiro, University of California San Diego, USA (thmosqueiro ‘@’ ucsd.edu) Jordi Fonollosa, Institute for Bioengineering of Catalunya, Spain (jfonollosa ‘@’ ibecbarcelona.eu) Nikolai Rulkov, University of California San Diego, USA ( nrulkov ‘@’ ucsd.edu ) Irene Rodriguez-Lujan, Universidad Autonoma de Madrid, Spain ( Irene.rodriguez ‘@’ uam.es ) Data Set Information: This dataset has recordings of a gas sensor array composed of 8 MOX gas sensors, and a temperature and humidity sensor. This sensor array was exposed to background home activity while subject to two different stimuli: wine and banana. The responses to banana and wine stimuli were recorded by placing the stimulus close to the sensors. The duration of each stimulation varied from 7min to 2h, with an average duration of 42min. This dataset contains a set of time series from three different conditions: wine, banana and background activity. There are 36 inductions with wine, 33 with banana and 31 recordings of background activity. One possible application is to discriminate among background, wine and banana. This dataset is composed of two files: HTsensordataset.dat (zipped), where the actual time series are stored, and the HTSensormetadata.dat, where metadata for each induction is stored. Each induction is uniquely identified by an id in both files. Thus, metadata for a particular induction can be easily found by matching columns id from each file. We also made available python scripts to exemplify how to import, organize and plot our data. The scripts are available on GitHub: https://github.com/gauravgawade951999/gauravgit For each induction, we include one hour of background activity prior to and after the stimulus presentation. Time series were recorded at one sample per second, with minor variations at some data points due to issues in the wireless communication. For details on which sensors were used and how the time series is organized, see Attribute Information below.

Nitinguptadu/Heroku-

this code is for self learning purpose. in this code we have deploy the a simple web app code with static file using flask on heroku server

Nitinguptadu/Heroku-Demo

Nitinguptadu/Image-classification-model-deployment-using-Flask-

image classifiacion model with flask using keras

Nitinguptadu/Image-to-text-using-object-detection-

This repo is for self learning purpose

Nitinguptadu/Keras-cnn-for-Regressiion-and-classification--with-Multi-Target-

This Repo is for self learning purpose

Nitinguptadu/keras-ocr-

this repo is for self learning purpose

Nitinguptadu/Multi-lable-classifiaction-on-tabluar-data-

This Project is for self learning Purpose

Nitinguptadu/Nitinguptadu-Predictive-Tests-For-Assessing-Risk-of-Cancer-Recurrence-

Machine Learning Flask CSS HTML Heroku Xgboost pycart

Nitinguptadu/Object-detection-

Use below directory structure, and generate text file containing (KITTI format ) annotation for each image. . ├── images │ ├── 000000.jpg │ . │ . │ └── XXXXXX.jpg └── annotations ├── 000000.txt . . └── XXXXXX.txt The text file should contain one annotation per line, with values separated by space. className 0.00 0 0.00 x1 y1 x2 y2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 className 0.00 0 0.00 x1 y1 x2 y2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Eg., person 0.00 0 0.00 20 50 196 600 0.00 0.00 0.00 0.00 0.00 0.00 0.00 face 0.00 0 0.00 100 120 150 180 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Note:- The dataset is available to download through Google drive & Kaggle , here's what can be done. Download it manually and upload to colab (https://www.kaggle.com/imneonizer/wider-person/download).

Nitinguptadu/Ploting-of-Desion-Boundary-in-python-3.0

This repo is for self learning purpose

Nitinguptadu/Python-Challenges-

This repo is for self learning purpose

Nitinguptadu/Speech-to-text-using-deep-learning-

This repo is for self learning purpose

Nitinguptadu/Time-series-EEg-Classification

This repo is for self learning purpose

Nitinguptadu/Yugen-ai-Heroku

Heroku

Nitinguptadu/Yugen.ai

Project

Nitinguptadu/-Predict-Future-Sales

Nitinguptadu/Machine-Learning-Models

Debug machine learning classifiers and explain their predictions

Nitinguptadu/Yugen-ai

Machine learning project