VGG-19 deep learning model trained using ISCX 2012 IDS Dataset
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Updated
Jan 5, 2019 - Jupyter Notebook
VGG-19 deep learning model trained using ISCX 2012 IDS Dataset
Fully automated end-to-end framework to extract data from bar plots and other figures in scientific research papers using modules such as OpenCV, AWS-Rekognition.
Pre-trained VGG-Net Model for image classification using tensorflow
VGG19 feature extractor using PyTorch framework
COVID-19 Detection From X-ray Images Using Deep Learning
Art Generation with Neural Style Transfer in 200 lines of code
Detected the crop diseases and differentiated between various crop diseases for a particular plant. Worked with appropriate neural network image classification algorithms like CNN, Inception-V3, VGG-16. and VGG-19
Implementation of 2D Neural Style Transfer in a manner proposed in an original paper, along with optimization for preserving content image colors.
This is the repository for the course Computational Imaging project with the subject: Combination of Convolutional Neural Network and Markov Random Field for image synthesis.
My university final year project 'An Implementation of Multiple Improvements to Neural Style Transfer.'
Links to my works, where a variety of generative models are implemented using TensorFlow and PyTorch. Among the implemented models are Autoencoder, VAE, GAN, Pix2Pix, among others.
Introduce and implement the Neural Style
A web application that classifies an uploaded image into a disaster type, utilizing Angular
Neural style transfer is a process of transferring style from an image to another using deep learning.
VGG-19 Signature Verification using Transfer Learning
Este projeto contém dois classificadores de imagens. O primeiro utilizando extração de características e o segundo utilizando fine-tunning.
This repo is about Transfer Learning Implementation using pretrained models(VGG16 & VGG19)
Prediction of cancer using Image Classification of Chest Xray scans.
Transfer style of an image to another using Convolutional Neural Networks.
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