sddaphal

anomaly-detection-using-autoencoders

This is the implementation of Semi-supervised Anomaly Detection using AutoEncoders

Apple-Leaf-Disease-Detection

Build a deep learning model that can classify the different types of diseases based on Apple leaf using image enhancement, segmentation, and feature extraction techniques.

autoencoder-anomaly-detection

Using an autoencoder neural net in Tensorflow to detect anomalies

AutoEncoder_vs_MetricLearning

AutoEncoder vs Metric Learning for Anomaly Detection

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

awesome-opencv

DigitalImageWatermarking

Project-2021

Image feature database creation of crop type identification

sddaphal/Project-2021

Image feature database creation of crop type identification

sddaphal/Apple-Leaf-Disease-Detection

Build a deep learning model that can classify the different types of diseases based on Apple leaf using image enhancement, segmentation, and feature extraction techniques.

sddaphal/Bottle-Inspection

This project simulates the inspection of shampoo bottles on a conveyor belt to verify if the bottle cap has been placed properly or not. It uses openCV to make the image processing and has an intuitive supervisory interface to display history of misplacement.

sddaphal/BySpire

🌱 This project aims to bridge the current gap between controlled-environment agriculture and the computer vision and robotics fields. The intended outcome is to develop an open-source toolkit for plant analysis that can be adapted to container-size smart farms, vertical farms, or even small window farms at home. The project will enable farmers world wide to automate and optimize their food production.

sddaphal/cancer_detection

CNN Cancer Detection Microscopic images HOG SIFT SURF LBP

sddaphal/CSE1002_C_CPP

This repository includes all the codes that were a part of the Object Oriented Programming Course - CSE1002. The Languages used are - C and CPP

sddaphal/demo

sample demo repository

sddaphal/Digital-Image-Processing

Digital image processing is introduced in detail. all code are implementing by OpenCV 3.4 and C++

sddaphal/Fault-Detection-for-Predictive-Maintenance-in-Industry-4.0

This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.

sddaphal/github-slideshow

A robot powered training repository :robot:

sddaphal/Graduation-Project-Detection-of-Plant-Leaves-Diseases

The project consists of an android application that is used by the house gardeners and anyone who owns a plant in their houses to take a picture of a plant or choose a picture from their mobile. Moreover, the application screens the image to detect a disease based on the disease symptoms by processing it in an image processing system and predict the type of disease and displays the result to the user.

sddaphal/image-classification-spatial

A novel architecture for enhancing image classification. Reference paper: https://arxiv.org/abs/2104.12294

sddaphal/Image-processing-algorithm

paper implement

sddaphal/krishi-unnati

A Mobile Application for Plant and Crop Disease Detection using Convolutional Neural Networks.

sddaphal/Leaf_Disease_Detection-Classification

Flask App Which detects 15 variety of plants [Pepper , Potato , Tomato ]

sddaphal/MaskDetection

This is the work done by mitaoe students

sddaphal/Multimodal-Image-Classifier

Image classifier for multimodal input (Image + non-Image features). Useful when we have additional data for images which may be useful for classification

sddaphal/PFLU-FPFLU

Codes of the paper "PFLU and FPFLU: Two novel non-monotonic activation functions in convolutional neural networks"

sddaphal/Plant-diseases-classification-using-ML-web-app-flutter-app

My project plan was based on an image dataset of the leaves of a specific group of crops called "Plant Village". I used this dataset to build four custom trained models using machine learning (ML) techniques. Next, I deployed these custom ML models to make them available to the end-users. These models are accessible via a website and via a nice android mobile application using Flutter

sddaphal/PlantsApp

Plants App is my graduation project built using android studio combined with deep learning code. It is an application to detect plants leaves disease using deep learning CNN model

sddaphal/Sugarcane

B.E Project

sddaphal/Swapnil-Resume

This repository contains the samples we used for creating resume

sddaphal/tactode_recognition

Code repository for Tactode tiles recognition using: (i) machine learning with HOG&SVM, (ii) deep learning with some neural networks like VGG16/19, ResNet152, MobileNetV2, YOLOv4 and SSD, (iii) matching of handcrafted features with SIFT, SURF, ORB and BRISK, and (iv) template matching.

sddaphal/tflite2onnx

Convert TensorFlow Lite models (*.tflite) to ONNX.

sddaphal/TIPL

template image processing library

sddaphal/Tomato-Leaf-Disease-Detection

A simple CNN model to detect and classify ten different types of tomato leaf disease.

sddaphal/Transfer_Learning_Comparision

Comparison of CNN Models by performing classification on Medical MNIST data-set via Transfer Learning.

sddaphal/Uday_information

This repository is just for test purpose

sddaphal/unsupervised_crack_segmentation

K-means clustering algorithm is trained over GLCM texture property features of the concrete images with crack to detect the crack location

sddaphal/useful_functions