The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at .

Overview

PixelNet: Representation of the pixels, by the pixels, and for the pixels.

We explore design principles for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that stratified sampling of pixels allows one to:

  1. add diversity during batch updates, speeding up learning;

  2. explore complex nonlinear predictors, improving accuracy;

  3. efficiently train state-of-the-art models tabula rasa (i.e., from scratch) for diverse pixel-labeling tasks.

Our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset, and edge detection on BSDS. We also demonstrate self-supervised representation learning via geometry. With even few data points, we achieve results better than previous approaches for unsupervised/self-supervised representation learning. More details are available on our project page.

If you found these codes useful for your research, please consider citing -

@article{pixelnet,
  title={PixelNet: {R}epresentation of the pixels, by the pixels, and for the pixels},
  author={Bansal, Aayush and Chen, Xinlei, and  Russell, Bryan and Gupta, Abhinav and Ramanan, Deva},
  Journal={arXiv preprint arXiv:1702.06506},
  year={2017}
}

How to use these codes?

Anyone can freely use our codes for what-so-ever purpose they want to use. Here we give a detailed instruction to set them up and use for different applications. We will also provide the state-of-the-art models that we have trained.

The codes can be downloaded using the following command:

git clone --recursive https://github.com/aayushbansal/PixelNet.git
cd PixelNet

Our codebase is built around caffe. We have included a pointer to caffe as a submodule.

ls tools/caffe

Our required layers are available within this submodule. To install Caffe, please follow the instructions on their project page.

Models

We give a overview of the trained models in models/ directory.

ls models

Experiments

We provide scripts to train/test models in experiments/ directory.

ls experiments

Python Codes

The amazing Xinlei Chen wrote a version of code in Python as well and wrote the GPU implementation of sampling layer. Check it out. We have not tested that extensively. It would be great if you could help us test that :)

Owner
Aayush Bansal
Aayush Bansal
Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)" which introduces a new class of deep generative models that gene

Guan-Horng Liu 43 Jan 03, 2023
Python/Rust implementations and notes from Proofs Arguments and Zero Knowledge

What is this? This is where I'll be collecting resources related to the Study Group on Dr. Justin Thaler's Proofs Arguments And Zero Knowledge Book. T

Thor 66 Jan 04, 2023
[ArXiv 2021] Data-Efficient Instance Generation from Instance Discrimination

InsGen - Data-Efficient Instance Generation from Instance Discrimination Data-Efficient Instance Generation from Instance Discrimination Ceyuan Yang,

GenForce: May Generative Force Be with You 93 Dec 25, 2022
Official source code of Fast Point Transformer, CVPR 2022

Fast Point Transformer Project Page | Paper This repository contains the official source code and data for our paper: Fast Point Transformer Chunghyun

182 Dec 23, 2022
PatrickStar enables Larger, Faster, Greener Pretrained Models for NLP. Democratize AI for everyone.

PatrickStar: Parallel Training of Large Language Models via a Chunk-based Memory Management Meeting PatrickStar Pre-Trained Models (PTM) are becoming

Tencent 633 Dec 28, 2022
A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

ffcv ImageNet Training A minimal, single-file PyTorch ImageNet training script designed for hackability. Run train_imagenet.py to get... ...high accur

FFCV 92 Dec 31, 2022
Research code for the paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models"

Introduction This repository contains research code for the ACL 2021 paper "How Good is Your Tokenizer? On the Monolingual Performance of Multilingual

AdapterHub 20 Aug 04, 2022
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection

SAGA Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection Please refer to the Jupyter notebook (Example.ipynb) for an example of using t

9 Dec 28, 2022
Official implementation of paper "Query2Label: A Simple Transformer Way to Multi-Label Classification".

Introdunction This is the official implementation of the paper "Query2Label: A Simple Transformer Way to Multi-Label Classification". Abstract This pa

Shilong Liu 274 Dec 28, 2022
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Daniel Bourke 3.4k Jan 07, 2023
Neural Nano-Optics for High-quality Thin Lens Imaging

Neural Nano-Optics for High-quality Thin Lens Imaging Project Page | Paper | Data Ethan Tseng, Shane Colburn, James Whitehead, Luocheng Huang, Seung-H

Ethan Tseng 39 Dec 05, 2022
My implementation of DeepMind's Perceiver

DeepMind Perceiver (in PyTorch) Disclaimer: This is not official and I'm not affiliated with DeepMind. My implementation of the Perceiver: General Per

Louis Arge 55 Dec 12, 2022
A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

StyleGAN3 CLIP-based guidance StyleGAN3 + CLIP StyleGAN3 + inversion + CLIP This repo is a collection of Jupyter notebooks made to easily play with St

Eugenio Herrera 176 Dec 30, 2022
Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation Experiment Setting: CIFAR10 (downloaded and saved in ./DATA

John Seon Keun Yi 38 Dec 27, 2022
The source code of "SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation", accepted to WACV 2022.

SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation The source code of our work "SIDE: Center-based Stereo 3D Detecto

10 Dec 18, 2022
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

TensorFlowOnSpark TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from the T

Yahoo 3.8k Jan 04, 2023
Official implementation of the paper 'High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network' in CVPR 2021

LPTN Paper | Supplementary Material | Poster High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network Ji

372 Dec 26, 2022
Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)

VIN: Value Iteration Networks A quick thank you A few others have released amazing related work which helped inspire and improve my own implementation

Kent Sommer 297 Dec 26, 2022
Official code for Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018)

MUC Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018) Performance Details for Accuracy: | Dataset

Yijun Su 3 Oct 09, 2022
Code for "Diffusion is All You Need for Learning on Surfaces"

Source code for "Diffusion is All You Need for Learning on Surfaces", by Nicholas Sharp Souhaib Attaiki Keenan Crane Maks Ovsjanikov NOTE: the linked

Nick Sharp 247 Dec 28, 2022