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SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale , University of California, Berkeley , and Stanford University . In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. [4] It is written in C++, with a Python interface. [5]
ITK was developed with funding from the National Library of Medicine as an open resource of algorithms for analyzing the images of the Visible Human Project. ITK stands for The Insight Segmentation and Registration Toolkit. The toolkit provides leading-edge segmentation and registration algorithms in two, three, and more dimensions. ITK uses ...
U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]
Mamba employs a hardware-aware algorithm that exploits GPUs, by using kernel fusion, parallel scan, and recomputation. [2] The implementation avoids materializing expanded states in memory-intensive layers, thereby improving performance and memory usage. The result is significantly more efficient in processing long sequences compared to ...
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...
A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior [3] A TensorFlow-based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow
Given an image D containing an instance of a known object category, e.g. cows, the OBJ CUT algorithm computes a segmentation of the object, that is, it infers a set of labels m. Let m be a set of binary labels, and let Θ {\displaystyle \Theta } be a shape parameter( Θ {\displaystyle \Theta } is a shape prior on the labels from a layered ...