/Inceptionv4_and_Inception-ResNetv2.PyTorch

A PyTorch implementation of Inception-v4 and Inception-ResNet-v2.

Primary LanguagePython

Introduction

An inofficial PyTorch implementation of Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Models

  • Inception-v4
  • Inception-ResNet-v2

Analysis

All the results reported here are based on this repo, and 50000 ImageNet validation sets。

  • top-1 accuracy
  • top-5 accuracy
  • # model parameters / FLOPs
  • inference time (average)
  • bottom10 accuracy
  • Hyper parameters
  • blacklists

  • Top-1 and top-5 accuracy with blacklisted entities

    Model top-1(TF) top-1(this repo) top-5(TF) top-5(this repo)
    Inception-v4 0.801 0.801 0.952 0.950
    Inception-ResNet-v2 0.804 0.803 0.953 0.951

  • Other hyper-parameters in Inception-v4

    eps in BatchNorm2d and count_include_pad in AvgPool2d

    Config #top-1 top-1 #top-5 top-5
    eps=0.001, count_include_pad=False 40041 0.801 47445 0.949
    eps=0.001, count_include_pad=True 39970 0.799 47395 0.948
    eps=1e-5, count_include_pad=False 40036 0.801 47438 0.949

  • Model parameters and FLOPs

    Model Params(M) FLOPs(G)
    Inception-v4 42.68 6.31
    Inception-ResNet-v2 55.84 6.65

  • Average inference time(RTX 2080Ti)

    Model Single inference time(ms)
    Inception-v4 40.54
    Inception-ResNet-v2 61.62

  • Top-1 and top-5 accuracy(bottom-10 classes)

    • Inception-v4

      Top-1 accuracy Classes Top-5 accuracy Classes
      0.16 n04152593 : screen, CRT screen 0.62 n03692522 : loupe, jeweler's loupe
      0.22 n04286575 : spotlight, spot 0.64 n04286575 : spotlight, spot
      0.22 n02123159 : tiger cat 0.64 n04525038 : velvet
      0.22 n03642806 : laptop, laptop computer 0.68 n04081281 : restaurant, eating house, eating place, eatery
      0.22 n04355933 : sunglass 0.72 n03532672 : hook, claw
      0.24 n04560804 : water jug 0.72 n03658185 : letter opener, paper knife, paperknife
      0.26 n04525038 : velvet 0.74 n03476684 : hair slide
      0.26 n02979186 : cassette player 0.74 n02910353 : buckle
      0.28 n02107908 : Appenzeller 0.76 n02776631 : bakery, bakeshop, bakehouse
      0.34 n03710637 : maillot 0.76 n03347037 : fire screen, fireguard

    • Inception-ResNet-v2

      Top-1 accuracy Classes Top-5 accuracy Classes
      0.18 n04152593 : screen, CRT screen 0.6 n04286575 : spotlight, spot
      0.22 n03710637 : maillot 0.64 n04525038 : velvet
      0.22 n02123159 : tiger cat 0.64 n03692522 : loupe, jeweler's loupe
      0.28 n02979186 : cassette player 0.66 n03658185 : letter opener, paper knife, paperknife
      0.28 n04008634 : projectile, missile 0.7 n04081281 : restaurant, eating house, eating place, eatery
      0.28 n04355933 : sunglass 0.72 n03532672 : hook, claw
      0.3 n03658185 : letter opener, paper knife, paperknife 0.74 n04591157 : Windsor tie
      0.3 n03642806 : laptop, laptop computer 0.74 n03016953 : chiffonier, commode
      0.3 n04286575 : spotlight, spot 0.74 n04239074 : sliding door
      0.32 n02089973 : English foxhound 0.74 n03476684 : hair slide

Inception-Resnet-v2 Architecture

  • Stem

  • Overall schema

    The output of the last Inception-ResNet-C layer has no ReLU activation.

Reference