Udacity DataScience nanodegree image classifier problem
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Updated
Jun 23, 2019 - HTML
Udacity DataScience nanodegree image classifier problem
Transfer learning using InceptionV3 Keras model for Chest X-Ray Classification
Constructed an algorithm that works on user supplied image. If a dog is detected, it estimates the breed of the dog, Trained using Transfer learning with CNN.
A Deep Learning Humerus Bone Fracture Detection Model which classifies a broken humerus bone X-ray image from a normal X-ray image with no fracture using Back Propagation, Regularization, Convolutional Neural Networks (CNN), Auto-Encoders (AE) and Transfer Learning.
Behavioral Cloning (project 4 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
An Azure based computer vision web app
Given an image, detects whether there is a human face or a dog in it. In case it is a dog, the algorithm classifies the breed of the dog. Deep Learning Nanodegree project.
Convolutional Neural Networks capable of classifying Normal vs. Pneumonia frontal chest radiograph (a set of 32 images in 8 seconds) using Transfer Learning with ResNet50
TFM-Lung-Disease-Classifier
Transfer Learning using InceptionResNetV2 to the Augmented Neuroimage Data for the Autism Spectrum Disorder (ASD) Classification, using ABIDE I dataset.
A transfer learning based COVID-19 lung x-ray classifier made into a WebApp!
Content-Based Image Retrieval System using multiple images deciphers for feature extraction
Multi-object Detection System for Plant Stomata Phenotypic Traits, release only, which has been published on IEEE/ACM-TCBB 2021 (CCF-B)
Identifying and classifying brain tumors in MRI scans using convolutional neural networks
INR Denomination Recognition is an image classification project
Waste image classification platform to promote sustainability | Created at HackMIT 2022
It is a autonomous robot equipped with sensors and cameras with deep learning algorithms to monitor and maintain crop health and act as an aid to farmers and huge estate or nursery owners
Deep neural network model combining audio signal processing and pre-trained audio CNN achieved 90.1% adjusted accuracy (27.6% improvement) for classifying audio recording environment.
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