Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/1511.00561v2.pdf (Except for the Upsampling layer where paper uses indices based upsampling which is not implemented in keras yet( I am working on it), but that shouldnt make a lot of difference). You can directly download the code from https://github.com/preddy5/segnet. This post is a explaination of what is happening in the code.
A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Overview
Owner
Pradyumna Reddy Chinthala
Transfer Learning for Pose Estimation of Illustrated Characters
bizarre-pose-estimator Transfer Learning for Pose Estimation of Illustrated Characters Shuhong Chen *, Matthias Zwicker * WACV2022 [arxiv] [video] [po
Flask101 - FullStack Web Development with Python & JS - From TAQWA
Task: Create a CLI Calculator Step 0: Creating Virtual Environment $ python -m
Experiments with differentiable stacks and queues in PyTorch
Please use stacknn-core instead! StackNN This project implements differentiable stacks and queues in PyTorch. The data structures are implemented in s
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au
This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR
This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR,which is an open-source toolbox based on PyTorch. The overall architecture will be sh
This package implements THOR: Transformer with Stochastic Experts.
THOR: Transformer with Stochastic Experts This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts. Installation
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
ademxapp Visual applications by the University of Adelaide In designing our Model A, we did not over-optimize its structure for efficiency unless it w
Supplementary code for TISMIR paper "Sliding-Window Pitch-Class Histograms as a Means of Modeling Musical Form"
Sliding-Window Pitch-Class Histograms as a Means of Modeling Musical Form This is supplementary code for the TISMIR paper Sliding-Window Pitch-Class H
LSTM model trained on a small dataset of 3000 names written in PyTorch
LSTM model trained on a small dataset of 3000 names. Model generates names from model by selecting one out of top 3 letters suggested by model at a time until an EOS (End Of Sentence) character is no
Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)
Progressive Transformers for End-to-End Sign Language Production Source code for "Progressive Transformers for End-to-End Sign Language Production" (B
Code for the paper "Functional Regularization for Reinforcement Learning via Learned Fourier Features"
Reinforcement Learning with Learned Fourier Features State-space Soft Actor-Critic Experiments Move to the state-SAC-LFF repository. cd state-SAC-LFF
Very Deep Convolutional Networks for Large-Scale Image Recognition
pytorch-vgg Some scripts to convert the VGG-16 and VGG-19 models [1] from Caffe to PyTorch. The converted models can be used with the PyTorch model zo
Ego4d dataset repository. Download the dataset, visualize, extract features & example usage of the dataset
Ego4D EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated v
Unofficial implementation (replicates paper results!) of MINER: Multiscale Implicit Neural Representations in pytorch-lightning
MINER_pl Unofficial implementation of MINER: Multiscale Implicit Neural Representations in pytorch-lightning. 📖 Ref readings Laplacian pyramid explan
NP DRAW paper released code
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation This repo contains the official implementation for the NP-DRAW paper.
PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambiguation for Partial Label Learning
PiCO: Contrastive Label Disambiguation for Partial Label Learning This is a PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambig
Framework for evaluating ANNS algorithms on billion scale datasets.
Billion-Scale ANN http://big-ann-benchmarks.com/ Install The only prerequisite is Python (tested with 3.6) and Docker. Works with newer versions of Py
Everything you need to know about NumPy( Creating Arrays, Indexing, Math,Statistics,Reshaping).
Everything you need to know about NumPy( Creating Arrays, Indexing, Math,Statistics,Reshaping).
Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min
VGGFace2-HQ - A high resolution face dataset for face editing purpose
The first open source high resolution dataset for face swapping!!! A high resolution version of VGGFace2 for academic face editing purpose