EfficientNetV2 implementation using PyTorch

Last update: Dec 29, 2022

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

EfficientNetV2 implementation using PyTorch

Related tags

Overview

Train Steps

Note

Owner

Jahongir Yunusov

Contenido del curso Bases de datos del DCC PUC versión 2021-2

Conversion between units used in magnetism

PyTorch implementation for 3D human pose estimation

Benchmark for evaluating open-ended generation

A medical imaging framework for Pytorch

Galileo library for large scale graph training by JD

keyframes-CNN-RNN(action recognition)

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

Distributional Sliced-Wasserstein distance code

Relative Human dataset, CVPR 2022

A real-time motion capture system that estimates poses and global translations using only 6 inertial measurement units

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

CONditionals for Ordinal Regression and classification in tensorflow

Diverse Branch Block: Building a Convolution as an Inception-like Unit

Implementation of ICCV2021(Oral) paper - VMNet: Voxel-Mesh Network for Geodesic-aware 3D Semantic Segmentation

The project covers common metrics for super-resolution performance evaluation.

Algorithmic trading using machine learning.

Face Recognition Attendance Project

A Vision Transformer approach that uses concatenated query and reference images to learn the relationship between query and reference images directly.

中文语音识别系列，读者可以借助它快速训练属于自己的中文语音识别模型，或直接使用预训练模型测试效果。