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You will get guidance on how to come to Super-Resolution using Low-Res Imagery in Python
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You will get guidance on how to come to Super-Resolution using Low-Res Imagery in Python
Top Rated
Project details
Nowadays, low-resolution imagery is abundant whereas high-resolution imagery is scarce. Being able to train a deep neural network to enhance the image resolution of low-resolution imagery will allow someone to take advantage of the high acquisition rates and this is the aim of this project.
Using Python, Pytorch, deep neural networks and open-source code this project allows users to train a combination of high- and low-resolution imagery to receive super-resolution images.
The price of this project is negotiable depending on the requirements.
Using Python, Pytorch, deep neural networks and open-source code this project allows users to train a combination of high- and low-resolution imagery to receive super-resolution images.
The price of this project is negotiable depending on the requirements.
AI Algorithms
Convolutional Neural Network, Generative Adversarial Network, StyleGANAI Applications
Image Analysis, Image Processing, Image UpscalingAI Development Language
PythonAI Tools
PyTorchAI Models
BERT$450
- Delivery Time 5 days
- Image Upscaling
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Lampros is very experienced in R. He has a lot of integrity, accuracy and quality in his code. He suggested improvements a long the way, and went above and beyond, understood the complexity of the requirement and managed it exceptionally well. The communication was great, he was available, finished his work on time and attentive to the budget. I definitely recommend him and hope to work in the future again.
About Lampros
R, Python, Remote Sensing, Deep Learning and Machine Learning Analyst
100%
Job Success
Paramythia, Greece - 5:29 pm local time
I work with R and Rstudio on a daily basis. I can work with machine learning algorithms based on almost all CRAN, Github or Gitlab repositories. I can utilize visualization R packages such as ggplot2, plotly, tmap, leaflet, mapview. I can create shiny applications (shiny.rstudio.com/gallery/) and report my results in .pdf, word, .html or any other available format using Rmarkdown.
Moreover, I'm capable of using the hybrid 'Rcpp' and 'RcppArmadillo' R packages to improve the efficiency of R code and the Keras and Pytorch deep learning libraries for regression, classification, object detection or image segmentation (with or without pre-trained models).
Steps for completing your project
After purchasing the project, send requirements so Lampros can start the project.
Delivery time starts when Lampros receives requirements from you.
Lampros works on your project following the steps below.
Revisions may occur after the delivery date.
Downloading imagery or usage of existing ones
Images will be downloaded or an existing dataset will be used
Creating a super-resolution deep neural network
Using open source code a super-resolution deep neural network will be created and trained