Search Results for: "CNN"

R-CNN vs Fast R-CNN vs Faster R-CNN – A Comparative Guide

R-CNNs ( Region-based Convolutional Neural Networks) a family of machine learning models Specially designed for object detection, the original goal of any R-CNN is to detect objects in any input image

Complete Guide to Transposed Convolutions in CNN Models

it is convenient to not change the size of output or keep the dimensions of input and output the same. This can be achieved by the transposed convolution in a better way

Guide to Different Padding Methods for CNN Models

the convolutional layers reduce the size of the output. So in cases where we want to increase the size of the output and save the information presented in the corners

Are Visual Transformers Better Than CNNs

So far, convolutional neural networks (CNNs) have been the de-facto model for visual data.

Guide To Text Classification using TextCNN

Text classification is a process of providing labels to the set of texts or words in one, zero or predefined labels format, and those labels will tell us about the sentiment of the set of words.

Inflated 3D CNN

Action Recognition Using Inflated 3D CNN

We all have audienced the fantastic deep learning approaches that have regularly or empirically, demonstrated…

Grand Theft Auto Gets A CNN Facelift

Researchers from Intel Lab have landscaped Grand Theft Auto V to make it look almost…

Is MLP Better Than CNN & Transformers For Computer Vision?

Earlier this month, Google researchers released a new algorithm called MLP-Mixer, an architecture based exclusively…

Tech Behind Google’s New CNN, EfficientNetV2

EfficientNetV2 can train up to 11x faster than prior models, while being up to 6.8x smaller in parameter size.

Why Transformers Are Increasingly Becoming As Important As RNN And CNN?

Google AI unveiled a new neural network architecture called Transformer in 2017. The GoogleAI team…

KGCNN and Ragged Tensor

Introduction To Keras Graph Convolutional Neural Network(KGCNN) & Ragged Tensor

KGCNN offers a straightforward and flexible integration of graph operations into the Tensorflow-Keras framework using RaggedTensors.

posecnn

Guide To 6D Object Pose Estimation Using PoseCNN

PoseCNN(Convolutional Neural Network) is an end to end framework for 6D object pose estimation, It…