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imagenet-validation.torch

A modified subset of https://github.com/soumith/imagenet-multiGPU.torch to allow easy testing of imagenet models.

Also used to make sure that models converted from other frameworks are correct.

This code was used in "An Analysis of Deep Neural Network Models for Practical Applications" report http://arxiv.org/abs/1605.07678

Evaluation

Model name top-1 (central crop) accuracy test time (s) framework trained by
NIN 0(?) 155.66 caffe authors
bn-NIN 62.62 170.71 torch me
inception-v2 51.00(?) 156.21 ? google
inception-v3 78.53 536.67 ? google
bvlc_alexnet 54.61 172.29 caffe bvlc
bn-alexnet 56.60 170.56 torch me
VGG_CNN_S 63.06 175.31 caffe VGG
VGG-16 70.62 738.82 caffe VGG
VGG-19 70.74 898.13 caffe VGG
ResNet-18 69.31 208.53 torch facebook
ResNet-34 72.94 363.11 torch facebook
ResNet-50 75.76 439.36 torch facebook
ResNet-101 77.34 695.78 torch facebook

Time is given for Titan Black.

Networks

  1. inception-v2: https://github.com/soumith/inception.torch
  2. inception-v3: https://github.com/szagoruyko/inception-v3.torch
  3. bvlc_alexnet: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet
  4. bn_alexnet: https://gist.github.com/szagoruyko/dd032c529048492630fc
  5. bn-nin: https://gist.github.com/szagoruyko/0f5b4c5e2d2b18472854
  6. VGG_CNN_S: https://gist.github.com/ksimonyan/fd8800eeb36e276cd6f9#file-readme-md
  7. VGG-16: https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
  8. VGG-19: https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md