Search Results for "image genetic algorithm"

Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior...

dupeGuru is a cross-platform GUI application written in Python (with Qt/Cocoa UI) that quickly detects duplicate files on your computer using flexible scanning modes—including filename fuzzy matching, content comparison, and specialized Music/Picture modes. On some linux systems pyrcc5 is not put on the path when installing python3-pyqt5, this will cause some issues with the resource files (and icons). These systems should have a respective pyqt5-dev-tools package, which should also be...

Crunch is an image compression tool for lossy PNG image file optimization. Using a combination of selective bit depth, color palette reduction and color type, as well as zopfli DEFLATE compression algorithm encoding that employs the pngquant and zopflipng PNG optimization tools, Crunch is effectively able to optimize and compress images with minimal decrease in image quality.

This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events...

DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups.

DWIP is short for Disk Wiping and Imaging Tool. This tool is being built for Mississippi State Universities National Forensics Training Center for use on a live cd to give out to it's students.