Leonard  Paucek

Leonard Paucek

1643223600

Pytorch Implementation Of Fast Neurotype

fast-neural-style :city_sunrise: :rocket:

NOTICE: This codebase is no longer maintained, please use the codebase from pytorch examples repository available at pytorch/examples/fast_neural_style.

This repository contains a pytorch implementation of an algorithm for artistic style transfer. The algorithm can be used to mix the content of an image with the style of another image. For example, here is a photograph of a door arch rendered in the style of a stained glass painting.

The model uses the method described in Perceptual Losses for Real-Time Style Transfer and Super-Resolution along with Instance Normalization. The saved-models for examples shown in the README can be downloaded from here.

DISCLAIMER: This implementation is also a part of the pytorch examples repository. Implementation in this repository uses pretrained Caffe2 VGG whereas the pytorch examples repository implementation uses pretrained Pytorch VGG. The two VGGs have different preprocessings which results in different --content-weight and --style-weight parameters. The styled output images also look slightly different.

Requirements

The program is written in Python, and uses pytorch, scipy. A GPU is not necessary, but can provide a significant speed up especially for training a new model. Regular sized images can be styled on a laptop, desktop using saved models.

Setup the environnment

Run with virtualenv

Create a virtualenv with python3.5 or python3.6. Older versions are not supported due to a lack of compatibilty with pytorch.

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run with Docker

Build the image:

docker build . -t fast-neural-style

Run the container:

docker run --rm --volume "$(pwd)/:/data" style eval --content-image /data/image.jpg --model /app/saved-models/mosaic.pth --output-image /data/output.jpg --cuda 0

Usage

Stylize image

python neural_style/neural_style.py eval --content-image </path/to/content/image> --model </path/to/saved/model> --output-image </path/to/output/image> --cuda 0
  • --content-image: path to content image you want to stylize.
  • --model: saved model to be used for stylizing the image (eg: mosaic.pth)
  • --output-image: path for saving the output image.
  • --content-scale: factor for scaling down the content image if memory is an issue (eg: value of 2 will halve the height and width of content-image)
  • --cuda: set it to 1 for running on GPU, 0 for CPU.

Train model

python neural_style/neural_style.py train --dataset </path/to/train-dataset> --style-image </path/to/style/image> --vgg-model-dir </path/to/vgg/folder> --save-model-dir </path/to/save-model/folder> --epochs 2 --cuda 1

There are several command line arguments, the important ones are listed below

  • --dataset: path to training dataset, the path should point to a folder containing another folder with all the training images. I used COCO 2014 Training images dataset [80K/13GB] (download).
  • --style-image: path to style-image.
  • --vgg-model-dir: path to folder where the vgg model will be downloaded.
  • --save-model-dir: path to folder where trained model will be saved.
  • --cuda: set it to 1 for running on GPU, 0 for CPU.

Refer to neural_style/neural_style.py for other command line arguments.

Models

Models for the examples shown below can be downloaded from here or by running the script download_styling_models.sh.



Author: abhiskk
Source Code: https://github.com/abhiskk/fast-neural-style
License: MIT License

#pytorch 

What is GEEK

Buddha Community

Pytorch Implementation Of Fast Neurotype

Implementing Real-time Object Detection System using PyTorch and OpenCV

Hands-On Guide to implement real-time object detection system using python

The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade.

Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together.

Python vs C++

Reading The Video Stream

Load the Model

Scoring a Single Frame

#artificial-intelligence #python #programming #implementing real-time object detection system #implementing real-time object detection system using pytorch and opencv #pytorch

Leonard  Paucek

Leonard Paucek

1643223600

Pytorch Implementation Of Fast Neurotype

fast-neural-style :city_sunrise: :rocket:

NOTICE: This codebase is no longer maintained, please use the codebase from pytorch examples repository available at pytorch/examples/fast_neural_style.

This repository contains a pytorch implementation of an algorithm for artistic style transfer. The algorithm can be used to mix the content of an image with the style of another image. For example, here is a photograph of a door arch rendered in the style of a stained glass painting.

The model uses the method described in Perceptual Losses for Real-Time Style Transfer and Super-Resolution along with Instance Normalization. The saved-models for examples shown in the README can be downloaded from here.

DISCLAIMER: This implementation is also a part of the pytorch examples repository. Implementation in this repository uses pretrained Caffe2 VGG whereas the pytorch examples repository implementation uses pretrained Pytorch VGG. The two VGGs have different preprocessings which results in different --content-weight and --style-weight parameters. The styled output images also look slightly different.

Requirements

The program is written in Python, and uses pytorch, scipy. A GPU is not necessary, but can provide a significant speed up especially for training a new model. Regular sized images can be styled on a laptop, desktop using saved models.

Setup the environnment

Run with virtualenv

Create a virtualenv with python3.5 or python3.6. Older versions are not supported due to a lack of compatibilty with pytorch.

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run with Docker

Build the image:

docker build . -t fast-neural-style

Run the container:

docker run --rm --volume "$(pwd)/:/data" style eval --content-image /data/image.jpg --model /app/saved-models/mosaic.pth --output-image /data/output.jpg --cuda 0

Usage

Stylize image

python neural_style/neural_style.py eval --content-image </path/to/content/image> --model </path/to/saved/model> --output-image </path/to/output/image> --cuda 0
  • --content-image: path to content image you want to stylize.
  • --model: saved model to be used for stylizing the image (eg: mosaic.pth)
  • --output-image: path for saving the output image.
  • --content-scale: factor for scaling down the content image if memory is an issue (eg: value of 2 will halve the height and width of content-image)
  • --cuda: set it to 1 for running on GPU, 0 for CPU.

Train model

python neural_style/neural_style.py train --dataset </path/to/train-dataset> --style-image </path/to/style/image> --vgg-model-dir </path/to/vgg/folder> --save-model-dir </path/to/save-model/folder> --epochs 2 --cuda 1

There are several command line arguments, the important ones are listed below

  • --dataset: path to training dataset, the path should point to a folder containing another folder with all the training images. I used COCO 2014 Training images dataset [80K/13GB] (download).
  • --style-image: path to style-image.
  • --vgg-model-dir: path to folder where the vgg model will be downloaded.
  • --save-model-dir: path to folder where trained model will be saved.
  • --cuda: set it to 1 for running on GPU, 0 for CPU.

Refer to neural_style/neural_style.py for other command line arguments.

Models

Models for the examples shown below can be downloaded from here or by running the script download_styling_models.sh.



Author: abhiskk
Source Code: https://github.com/abhiskk/fast-neural-style
License: MIT License

#pytorch 

PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

Facebook Gives Away This PyTorch Library For Differential Privacy

Recently, Facebook AI open-sourced a new high-speed library for training PyTorch models with differential privacy (DP) known as Opacus. The library is claimed to be more scalable than existing state-of-the-art methods.

According to the developers at the social media giant, differential privacy is a mathematically rigorous framework for quantifying the anonymisation of sensitive data. With the growing interest in the machine learning (ML) community, this framework is often used in analytics and computations.

Differential privacy constitutes a strong standard for privacy guarantees for algorithms on aggregate databases. It is usually defined in terms of the application-specific concept of adjacent databases. The framework has several properties that make it particularly useful in applications, such as group privacy, robustness to auxiliary information, among others.

#developers corner #differential privacy #facebook ai research #facebook differential privacy #opacus #pytorch #pytorch library #pytorch library opacus

Fast Fit Keto - {Update 2021} Review,Price & Read More!

**Purchase Now !!! Snap on the Link beneath for more data. Rush !!!
**
Official Website:- http://wintersupplement.com/fast-fit-keto/

Fast Fit Keto Reviews – Everyone ought to lessen their weight. On the off chance that you could get thinner in a couple of days without contributing energy or exertion, you would. That is the reason incalculable individuals are taking Fast Fit Keto pills to consume fat quicker and simpler than at any other time. With this extraordinary keto formula, your body will get just the fixings it needs to become accustomed to ketosis so you can begin getting more fit immediately. In the primary month, you can shed five pounds or more. Come these lines through our Fast Fit Keto audit to discover how this astounding ketogenic weight reduction supplement can assist you with getting in shape quicker and simpler than at any other time in late memory. Something else, click on the example underneath to check whether you can ensure a 40% Discounted offer of the top rated ketogenic pills for weight reduction before the arrangement closures or supplies run out.

**What is Fast Fit Keto audits? **

Getting the best and flawless shape has been the normal longing for everyone The world has been encountering a gigantic transition of getting a hot shape, yet an awful way of life and numerous different things influence their cravings. Yet, there are supplements like Body Fast Fit Keto surveys a nature-based thing which has discernibly bring back the lost gracefulness of the body by managing unfortunate fats from the body. This thing has various properties and can be utilized in different issues.

Fast Fit Keto Reviews cost In bygone eras when there were not strong helpful focuses available, this plant has been used to treat heart issues and sometimes in excessively touchy conditions. It is local and generally, used as a piece of Ayurveda treatment. As demonstrated by experts this thing constructs the immunity, endurance, seethes fat, and augmentation slant mass. This enhancement show rapidly after use and starts dissolving the gathering of extra fat present on the body. The two guys and females can use this thing to make a slim and engaging body. Consequently you should go for this thing and endeavor to change your personality.

http://wintersupplement.com/fast-fit-keto/

https://www.stageit.com/fastfitketobuy

https://dribbble.com/fastfitketobuy

https://linktr.ee/fastfitketoreviews

https://www.startus.cc/company/fast-fit-keto-shark-tank

https://secure.aspca.org/team/fast-fit-keto-reviews

https://www.facebook.com/sharktankdietsreviews/posts/1496584900547209

https://thenevadaview.com/fast-fit-keto/

https://www.tripoto.com/trip/fast-fit-keto-shark-tank-dont-buy-diet-pill-before-reading-601cd9f201210

https://k12.instructure.com/eportfolios/20408/

https://twitter.com/FastFitKetoSha1

https://www.facebook.com/supplementsworldofficial/videos/134442571858804/

https://youtu.be/XxpD-VxgYcE

https://www.reddit.com/user/fastfitreview/comments/ld1c06/fast_fit_keto_review_scam_or_legit_where_to/

https://zenodo.org/record/4506017#.YBzp0fnhUdU

https://www.completefoods.co/diy/recipes/fast-fit-keto-update-2021-user-exposed-truth-read-now

https://fastfitketosharktank.medium.com/fast-fit-keto-fast-fit-keto-shark-tank-pills-reviews-side-effects-price-e7684f9a4493

https://fastfitketo1.medium.com/fast-fit-keto-reviews-shark-tank-benefits-scam-2021-price-5b42d99587ec

https://gocrowdera.com/US/other/fast-fit-keto/

https://sites.google.com/view/fast-fit-keto-shark-tank/

https://talknchat.net/read-blog/5805_fast-fit-keto-shark-tank-final-verdict-2021.html

http://snomoto.com/fast-fit-keto-reviews-pills-shark-tank-scam-where-to-buy/

https://www.docdroid.net/IoNNGnO/fast-fit-keto-shark-tank-pdf

https://www.docdroid.net/g8hM6Ww/fast-fit-keto-reviews-pdf

#fast fit keto shark tank #fast fit keto reviews #fast fit keto #fast fit keto reviews 2021