This repository is a part of the project for the course Computer Vision at the University of Zaragoza. The goal of the project is to classify landmarks using Neural Networks.
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Updated
May 16, 2023 - Jupyter Notebook
This repository is a part of the project for the course Computer Vision at the University of Zaragoza. The goal of the project is to classify landmarks using Neural Networks.
Automated Web-based Malaria Detection System Using Machine Learning, Deep Learning and Transfer Learning Techniques: A Comparative Analaysis
Universal Image classifier
As part of a Cloud and Distributed Systems lecture, we developed a scalable application in the form of an image classifier meant for deployment on Kubernetes. We also created our own Kubernetes infrastructure bare metal server, complete with loadbalancing and ingress-support.
This notebook is my attempt at predicting ages of children from the X-Ray images of their hands.
Workshop CDK Template to provision infra for the Deep Visual Search workshop
In this project, I built a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, my algorithm identifies an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. I submitted this project as part of Udacity's Machien Le…
Stanford dogs dataset breed classification with Xception (CNN)
Retina OCT Images Different Model Train
Apple leaf diseases classification project.
Udacity Machine Learning Engineer Nanodegree Convolutional Neural Networks (CNN) Project: Dog Breed Classifier
Recyclables manager fine-tuned on XCeption using Kaggle's GPU instances.
🐠 Recognition of 20 Mediterranean fish species using different state-of-the-art CNNs architectures, transfer learning and data augmentation.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Chainer implementation of the paper "Xception: Deep Learning with Depthwise Separable Convolutions" (https://arxiv.org/abs/1610.02357).
Identify dog or cat with deep learning
Image Models (VGG, ResNet, MobileNet V1, MobileNet V2, Xception Net, DenseNet and more) from scratch.
All the Computer Vision Models I've worked on
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