EfficientNetV2 implementation using PyTorch

Related tags

Deep Learningtrainingimagenetpytrochefficientnetv2
Overview

EfficientNetV2-S implementation using PyTorch

Train Steps

  • Configure imagenet path by changing data_dir in train.py
  • python main.py --benchmark for model information
  • python -m torch.distributed.launch --nproc_per_node=$ main.py --train for training model, $ is number of GPUs
  • python main.py --test for testing

Note

  • The model achieved 82.7 top-1 after 150 epochs
Number of parameters: 23941296
Time per operator type:
        778.049 ms.    70.6258%. Conv
        255.227 ms.    23.1677%. Sigmoid
          56.91 ms.    5.16589%. Mul
         6.1573 ms.   0.558916%. Add
        4.69289 ms.   0.425987%. ReduceMean
       0.613303 ms.  0.0556713%. FC
        1101.65 ms in Total
FLOP per operator type:
         17.277 GFLOP.    99.7074%. Conv
      0.0419251 GFLOP.   0.241954%. Mul
     0.00519322 GFLOP.  0.0299706%. Add
       0.003585 GFLOP.  0.0206894%. FC
        17.3277 GFLOP in Total
Feature Memory Read per operator type:
        295.875 MB.    50.5134%. Mul
        241.136 MB.     41.168%. Conv
        41.5457 MB.     7.0929%. Add
        7.17917 MB.    1.22567%. FC
        585.737 MB in Total
Feature Memory Written per operator type:
          167.7 MB.    49.2361%. Mul
        152.127 MB.    44.6639%. Conv
        20.7729 MB.    6.09882%. Add
          0.004 MB. 0.00117438%. FC
        340.605 MB in Total
Parameter Memory per operator type:
        87.8034 MB.    92.4486%. Conv
          7.172 MB.    7.55143%. FC
              0 MB.          0%. Add
              0 MB.          0%. Mul
        94.9754 MB in Total
Owner
Jahongir Yunusov
Jahongir Yunusov
GitHub Repository
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T

Xavier 33 Oct 12, 2022
[AAAI-2022] Official implementations of MCL: Mutual Contrastive Learning for Visual Representation Learning

Mutual Contrastive Learning for Visual Representation Learning This project provides source code for our Mutual Contrastive Learning for Visual Repres

winycg 48 Jan 02, 2023
RANZCR-CLiP 7th Place Solution

RANZCR-CLiP 7th Place Solution This repository is WIP. (18 Mar 2021) Installation git clone https://github.com/analokmaus/kaggle-ranzcr-clip-public.gi

Hiroshechka Y 21 Oct 22, 2022
For IBM Quantum Challenge Africa 2021, 9 September (07:00 UTC) - 20 September (23:00 UTC).

IBM Quantum Challenge Africa 2021 To ensure Africa is able to apply quantum computing to solve problems relevant to the continent, the IBM Research La

Qiskit Community 48 Dec 25, 2022
TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture.

A Brain Tumor Detection and Classification Model built using RESNET50 architecture. The model is also deployed as a web application using Flask framework.

Pranav Khurana 0 Aug 17, 2021
Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)

NDQ: Learning Nearly Decomposable Value Functions with Communication Minimization Note This codebase accompanies paper Learning Nearly Decomposable Va

Tonghan Wang 69 Nov 26, 2022
A repository with exploration into using transformers to predict DNA ↔ transcription factor binding

Transcription Factor binding predictions with Attention and Transformers A repository with exploration into using transformers to predict DNA ↔ transc

Phil Wang 62 Dec 20, 2022
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

============================================================================================================ `MILA will stop developing Theano https:

9.6k Jan 06, 2023
FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation

FIRA is a learning-based commit message generation approach, which first represents code changes via fine-grained graphs and then learns to generate commit messages automatically.

Van 21 Dec 30, 2022
DLWP: Deep Learning Weather Prediction

DLWP: Deep Learning Weather Prediction DLWP is a Python project containing data-

Kushal Shingote 3 Aug 14, 2022
Vehicle speed detection with python

Vehicle-speed-detection In the project simulate the tracker.py first then simulate the SpeedDetector.py. Finally, a new window pops up and the output

3 Dec 15, 2022
Neural Surface Maps

Neural Surface Maps Official implementation of Neural Surface Maps - Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra [Paper] [Project Page]

Luca Morreale 49 Dec 13, 2022
BC3407-Group-5-Project - BC3407 Group Project With Python

BC3407-Group-5-Project As the world struggles to contain the ever-changing varia

1 Jan 26, 2022
Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"

NTIRE2017 Super-resolution Challenge: SNU_CVLab Introduction This is our project repository for CVPR 2017 Workshop (2nd NTIRE). We, Team SNU_CVLab, (B

Bee Lim 625 Dec 30, 2022
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
A-ESRGAN aims to provide better super-resolution images by using multi-scale attention U-net discriminators.

A-ESRGAN: Training Real-World Blind Super-Resolution with Attention-based U-net Discriminators The authors are hidden for the purpose of double blind

77 Dec 16, 2022
Multitask Learning Strengthens Adversarial Robustness

Multitask Learning Strengthens Adversarial Robustness

Columbia University 15 Jun 10, 2022
Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Emile van Krieken 140 Dec 30, 2022
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

Deformable 3D Convolution for Video Super-Resolution Pytorch implementation of l

Xinyi Ying 28 Dec 15, 2022
Implementation for Simple Spectral Graph Convolution in ICLR 2021

Simple Spectral Graph Convolutional Overview This repo contains an example implementation of the Simple Spectral Graph Convolutional (S^2GC) model. Th

allenhaozhu 64 Dec 31, 2022