Compute CNN receptive field size in pytorch in one line
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Updated
May 9, 2024 - Python
Compute CNN receptive field size in pytorch in one line
Seamless analysis of your PyTorch models (RAM usage, FLOPs, MACs, receptive field, etc.)
A toolbox for receptive field analysis and visualizing neural network architectures
Revisiting Image Deblurring with an Efficient ConvNet - An efficient CNN performs better than Transformer
Numerical Computation of Receptive Field in Pytorch
High-Performance Transformers for Table Structure Recognition Need Early Convolutions
[BMVC 2024] Official repository of the paper titled "MSA^2 Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation"
An implementation of Olshausen and Field (96) in PyTorch
A Python 3 toolbox for neural receptive field estimation using splines and Gaussian priors.
Implementation of research paper : "PraNet: Parallel Reverse Attention Network for Polyp Segmentation" in Tensorflow
An implementation of DetNet with Keras.
Extract the receptive field of a fully connected cnn.
Python program to calculate and visualize effective receptive field of a layer in deep convolution neural network
Compute the theoretical/analytical receptive field of deep neural networks in plain Python.
Often we spend lots of time calculating the Receptive field of a CNN model.This Module can calculate the receptive field, Output image size from a model object
Numerically compute the Receptive Field of a conv block in PyTorch
This repo contains submissions of all assignments of a EVA by TSAI
A fast and versatile implementation of spike-triggered non-negative matrix factorization (STNMF) based on accelerated/fast HALS algorithms
This repository serves as a collection of implementations and resources for various computational neuroscience techniques, including Hopfield's network,hebbian learning and common spatial patterns etc.
Program implements a convolutional neural network for classifying images of numbers in the MNIST dataset as either even or odd using GPU framework.
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