PixForgeOn: (NST)Style-Blend Magic! 🎨 using tensorflow
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Updated
Nov 25, 2023 - Python
PixForgeOn: (NST)Style-Blend Magic! 🎨 using tensorflow
Neural style transfer (NST) is an optimization technique which takes two images, a Content image (the one you want to edit) and a style quotation image, and combine them together so the resultant image looks like the content image, but “edited” in the style of the style quotation image.
An implementation of flower search using VGG19 and KNN, with image dataset scraped from Google Image
A implemention of "A Neural Algorithm of Artistic Style".
Neural style transfer implementation.
This model will help you to detect the facial expression from a Image and show the result in a form of Emoji
Trap Camera with positioning system and classification of images by animal species
Developing a framework for early detection of Alzheimer’s Disease using DeepLearning techniques by implementing a VGG19 pre-trained model for MRI images.
This work applies classification and comparison concepts of a convulsive neural network created and trained from scratch, and compares it with existing network architectures such as a VGG19 and a Densenet applying transfer learning.
Implementation of the paper "A Neural Algorithm of Artistic Style" in TensorFlow
Specific image manipulations and inference with openCV and VGG19 with a simple UI.
Provides VGG networks to do things like this: r11, r31, r51 = net.forward(targets=['relu1_1', 'relu3_1', 'relu5_1']). Nuff said. :)
Implementation of Neural Style Transfer algorithm with PyTorch library
Red neuronal VGG19 para el cambo de estilo artístico
Lung segmentation is done to aid in the diagnosis of lung diseases and help prevent future health issues. CNN algorithms will be used here and we shall compare the accuracies among the three models.
Hot dog image classifiers for Kaggle competition
Transfer style from two different images with different weights onto a content image
Topic: Image Classification Using Guitar Code Grip Recognition Model // Summary: Modeled an algorithm that shows the highest classification accuracy on a dataset of grip images for 35 guitar chords
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