This repo is about Transfer Learning Implementation using pretrained models(VGG16 & VGG19)
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Updated
Apr 3, 2020 - Jupyter Notebook
This repo is about Transfer Learning Implementation using pretrained models(VGG16 & VGG19)
This repository houses all my code for the Course "Machine Learning for Image Processing" taken in Fall 2019 in UCSD
A repository to collaborate on the Course Project for ECE285 course "Machine Learning for Image Processing" taken at UCSD in the Fall Quarter of 2019
ENPM673 Project 4 - Optical Flow and VGG-16 CNN
Monkey species classification on the Kaggle dataset using CNNs
Leveraging advanced image processing and deep learning, this project classifies plant images using a subset of the Plant Seedlings dataset. The dataset includes diverse plant species captured under varying conditions. This project holds significance within my Master's in Computer Vision at uOttawa (2023).
App taking image as an input and using CNN network to identify dog breed
A project on building deep learning classifier to classify playing cards
Objective was to identify and benchmark several advanced convolutional neural network (CNN) architectures for distinguishing the full ASL alphabet, and then to embed the most accurate model into a live recognition stream driven by a webcam and MediaPipe hand tracking.
A project About Covid-19 Detection Using Various Deep Learning Algo.
Implementations of popular Convolutional Neural Networks (CNNs) for image classification and learning. Includes AlexNet, VGG, ResNet, Inception-v1/v3 and more — ideal for study and experimentation.
Web interface for object recognition by manually providing RoI (Region of Interests).
Our system works on the detection of cataracts and type of classification on the basis of severity namely; mild, normal, and severe, in an attempt to reduce errors of manual detection of cataracts in the early ages using Machine Learning and Transfer Learning
American Sign Language Classification Model
A PyTorch implementation of CNNs and transfer learning on CIFAR-10 Dataset and lots of experimentations.
Very Deep Convolutional Networks for Large-Scale Visual Recognition
🏥 AI-powered medical imaging system using CNN for automated disease detection. Features brain tumor classification (Glioma, Meningioma, Pituitary) from MRI scans with 97% accuracy. Extensible framework for multiple diseases with clinical-grade performance and interpretable results.
Brain tumor Detection and Classification using Magnetic Resonance Images
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