Official Code for "Non-deep Networks"

Related tags

Deep LearningNonDeepNetworks
Overview

Non-deep Networks
arXiv:2110.07641
Ankit Goyal, Alexey Bochkovskiy, Jia Deng, Vladlen Koltun

Overview: Depth is the hallmark of DNNs. But more depth means more sequential computation and higher latency. This begs the question -- is it possible to build high-performing ``non-deep" neural networks? We show that it is. We show, for the first time, that a network with a depth of just 12 can achieve top-1 accuracy over 80% on ImageNet, 96% on CIFAR10, and 81% on CIFAR100. We also show that a network with a low-depth (12) backbone can achieve an AP of 48% on MS-COCO.

If you find our work useful, please consider citing it:

@article{goyal2021nondeep,
  title={Non-deep Networks},
  author={Goyal, Ankit and Bochkovskiy, Alexey and Deng, Jia and Koltun, Vladlen},
  journal={arXiv:2110.07641},
  year={2021}
}

Code Coming Soon!

Comments
  • when will the code of the model be released?

    when will the code of the model be released?

    I am very interested in your research, when will the code of the model be released? I saw on October 23rd that you said it would be released in 4 weeks

    opened by Dr-Goopher 6
  • When will the code be released?

    When will the code be released?

    I am very interested in your work and would like to further study. I hope you can release the code as soon as possible in your busy schedule. Thank you！

    opened by SenShu96 5
  • what is the meaning of 'Shuffle' of fusion block in Fig. A1?

    what is the meaning of 'Shuffle' of fusion block in Fig. A1?

    Hello. Thank you for your great study. I wonder the meaning of 'Shuffle' of fusion block in Fig. A1. Is it pixel shuffle layer? Please let me know the meaning of that.

    Thank you.

    opened by jhcha08 3
  • Question about SSE module

    Question about SSE module

    Hi. Figure 2b shows that there's one 1x1conv in a branch of SSE, how to match the channel of output by 1x1conv with the channel of input after shortcut? If I set the output channel of 1x1conv the same as input, the channels of the outputs by RepVGG block and SSE will not match.

    opened by Tsianmy 2
  • Really faster than ResNet? I am very confused

    Really faster than ResNet? I am very confused

    Hello, my friend, appreciate for your great work! I have tested the code on https://github.com/Pritam-N/ParNet by Pritam-N and change the ResNet code in my model by using your ParNet , but the actual time is quite slow than the paper said. My block size is [64, 128, 256, 512, 2048], and the time of "forward()" is more than 5s average while the Resnet is 0.02s in my device. I have use the time function for every line in the forward(), find that the encode stuff is the main reason. I continue write time.perf_counter() in the encode stuff, find that the "self.stream2_fusion" and "self.stream3_fusion" is the most time user. Do you know why ?

    opened by StonepageVan 1
  • fusion module, accuracy about cifar100

    fusion module, accuracy about cifar100

    1. what is your shuffle code in your fusion module?
    2. what is your model architecture in cifar-100? I just changed front two downsample modules based on the ParNet for Imagenet in the paper. But the accuracy is lower. And How do you set the LR, MILESTONES and NUM_EPOCH to meet high accuracy?
    opened by qq769852576 2
Owner
Ankit Goyal
Phd Candidate @Princeton | Works in CV and AI
Ankit Goyal
GitHub Repository
Latte: Cross-framework Python Package for Evaluation of Latent-based Generative Models

Cross-framework Python Package for Evaluation of Latent-based Generative Models Latte Latte (for LATent Tensor Evaluation) is a cross-framework Python

Karn Watcharasupat 30 Sep 08, 2022
Code for "The Intrinsic Dimension of Images and Its Impact on Learning" - ICLR 2021 Spotlight

dimensions Estimating the instrinsic dimensionality of image datasets Code for: The Intrinsic Dimensionaity of Images and Its Impact On Learning - Phi

Phil Pope 41 Dec 10, 2022
Network Enhancement implementation in pytorch

network_enahncement_pytorch Network Enhancement implementation in pytorch Research paper Network Enhancement: a general method to denoise weighted bio

Yen 1 Nov 12, 2021
Residual Pathway Priors for Soft Equivariance Constraints

Residual Pathway Priors for Soft Equivariance Constraints This repo contains the implementation and the experiments for the paper Residual Pathway Pri

Marc Finzi 13 Oct 12, 2022
working repo for my xumx-sliCQ submissions to the ISMIR 2021 MDX

Music Demixing Challenge - xumx-sliCQ This repository is the GitHub mirror of my working submission repository for the AICrowd ISMIR 2021 Music Demixi

4 Aug 25, 2021
Implementation of popular SOTA self-supervised learning algorithms as Fastai Callbacks.

Self Supervised Learning with Fastai Implementation of popular SOTA self-supervised learning algorithms as Fastai Callbacks. Install pip install self-

Kerem Turgutlu 276 Dec 23, 2022
Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021) Tensorflow implementation of Bridging the Gap between Label- and Reference-ba

huangqiusheng 8 Jul 13, 2022
Parallel Latent Tree-Induction for Faster Sequence Encoding

FastTrees This repository contains the experimental code supporting the FastTrees paper by Bill Pung. Software Requirements Python 3.6, NLTK and PyTor

Bill Pung 4 Mar 29, 2022
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

967 Jan 04, 2023
Regulatory Instruments for Fair Personalized Pricing.

Fair pricing Source code for WWW 2022 paper Regulatory Instruments for Fair Personalized Pricing. Installation Requirements Linux with Python = 3.6 p

Renzhe Xu 6 Oct 26, 2022
Synthetic structured data generators

Join us on What is Synthetic Data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the stati

YData 850 Jan 07, 2023
SimpleDepthEstimation - An unified codebase for NN-based monocular depth estimation methods

SimpleDepthEstimation Introduction This is an unified codebase for NN-based monocular depth estimation methods, the framework is based on detectron2 (

8 Dec 13, 2022
Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Keras implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping

Yam Peleg 63 Sep 21, 2022
mPose3D, a mmWave-based 3D human pose estimation model.

mPose3D, a mmWave-based 3D human pose estimation model.

KylinChen 35 Nov 08, 2022
Blender Add-On for slicing meshes with planes

MeshSlicer Blender Add-On for slicing meshes with multiple overlapping planes at once. This is a simple Blender addon to slice a silmple mesh with mul

52 Dec 12, 2022
Time Series Forecasting with Temporal Fusion Transformer in Pytorch

Forecasting with the Temporal Fusion Transformer Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invari

Nicolás Fornasari 6 Jan 24, 2022
TensorFlow CNN for fast style transfer

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! It takes 100ms on a 2015 Titan X to style t

1 Dec 14, 2021
Code for ICCV 2021 paper Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes using Scene Graphs

Graph-to-3D This is the official implementation of the paper Graph-to-3d: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs | arx

Helisa Dhamo 33 Jan 06, 2023
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

What's New Below we share, in reverse chronological order, the updates and new releases in VISSL. All VISSL releases are available here. [Oct 2021]: V

Meta Research 2.9k Jan 07, 2023
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems - NAACL 2021

Fine-grained Post-training for Multi-turn Response Selection Implements the model described in the following paper Fine-grained Post-training for Impr

Janghoon Han 83 Dec 20, 2022