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All Projects → zeiss-microscopy → Bsconv

zeiss-microscopy / Bsconv

Licence: bsd-3-clause-clear
Reference implementation for Blueprint Separable Convolutions (CVPR 2020)

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python
139335 projects - #7 most used programming language

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deep-learningpytorchimage-classificationresnetmobilenetcifar10

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You can now find us at CVPR 2020. Our live Q&A sessions are on June 18, 2020 @ 5pm - 7pm PDT (click here to join) and June 19, 2020 @ 5am - 7am PDT (click here to join). We are looking forward to seeing you at CVPR!

CVPR 2020 is now over, and we thank you for all the interesting discussions! Our presentation video is available on YouTube. We will continue the development of the code and models in this repository, so stay tuned!

Blueprint Separable Convolutions (BSConv)

This repository provides code and trained models for the CVPR 2020 paper (official, arXiv):

Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets

Daniel Haase*, Manuel Amthor*

Teaser GIF

Table of Contents

  1. Overview
  2. Results
    1. CIFAR10 - MobileNetV1
    2. CIFAR10 - MobileNetV2
    3. CIFAR10 - MobileNetV3-small
    4. CIFAR10 - MobileNetV3-large
    5. CIFAR10 - WRN-16
    6. CIFAR10 - WRN-28
    7. CIFAR10 - WRN-40
    8. CIFAR100 - MobileNetV1
    9. CIFAR100 - MobileNetV2
    10. CIFAR100 - MobileNetV3-small
    11. CIFAR100 - MobileNetV3-large
    12. CIFAR100 - ResNets
    13. CIFAR100 - WRN-16
    14. CIFAR100 - WRN-28
    15. CIFAR100 - WRN-40
  3. Requirements
  4. Installation
  5. Usage
  6. Change Log
  7. Citation

Results

CIFAR10 - MobileNetV1

CIFAR10 MobileNetV1 Params Plot CIFAR10 MobileNetV1 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv1_w1 93.57 3.22 179.34
cifar_mobilenetv1_w3d4 93.51 1.82 102.66
cifar_mobilenetv1_w1d2 92.44 0.82 47.21
cifar_mobilenetv1_w1d4 91.17 0.22 12.99
cifar_mobilenetv1_w1_bsconvu 94.48 3.22 254.64
cifar_mobilenetv1_w3d4_bsconvu 94.38 1.82 144.98
cifar_mobilenetv1_w1d2_bsconvu 93.45 0.82 65.98
cifar_mobilenetv1_w1d4_bsconvu 92.13 0.22 17.66

CIFAR10 - MobileNetV2

CIFAR10 MobileNetV2 Params Plot CIFAR10 MobileNetV2 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv2_w1 93.91 2.24 92.40
cifar_mobilenetv2_w3d4 93.76 1.36 55.13
cifar_mobilenetv2_w1d2 92.55 0.70 27.32
cifar_mobilenetv2_w1d4 89.93 0.25 8.97
cifar_mobilenetv2_w1_bsconvs_p1d6 94.47 2.24 92.40
cifar_mobilenetv2_w3d4_bsconvs_p1d6 94.16 1.36 55.13
cifar_mobilenetv2_w1d2_bsconvs_p1d6 93.30 0.70 27.32
cifar_mobilenetv2_w1d4_bsconvs_p1d6 90.60 0.25 8.97

CIFAR10 - MobileNetV3-small

CIFAR10 MobileNetV3-small Params Plot CIFAR10 MobileNetV3-small FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv3_small_w1 92.57 1.09 18.48
cifar_mobilenetv3_small_w3d4 91.46 0.72 11.40
cifar_mobilenetv3_small_w1d2 90.33 0.44 6.00
cifar_mobilenetv3_small_w7d20 88.75 0.31 3.45
cifar_mobilenetv3_small_w1_bsconvs_p1d6 93.06 1.09 18.48
cifar_mobilenetv3_small_w3d4_bsconvs_p1d6 92.10 0.72 11.40
cifar_mobilenetv3_small_w1d2_bsconvs_p1d6 90.58 0.44 6.00
cifar_mobilenetv3_small_w7d20_bsconvs_p1d6 89.04 0.31 3.45

CIFAR10 - MobileNetV3-large

CIFAR10 MobileNetV3-large Params Plot CIFAR10 MobileNetV3-large FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv3_large_w1 94.38 2.98 68.45
cifar_mobilenetv3_large_w3d4 94.00 1.73 40.67
cifar_mobilenetv3_large_w1d2 93.30 0.82 20.00
cifar_mobilenetv3_large_w7d20 92.16 0.44 10.89
cifar_mobilenetv3_large_w1_bsconvs_p1d6 94.81 2.98 68.45
cifar_mobilenetv3_large_w3d4_bsconvs_p1d6 94.34 1.73 40.67
cifar_mobilenetv3_large_w1d2_bsconvs_p1d6 93.85 0.82 20.00
cifar_mobilenetv3_large_w7d20_bsconvs_p1d6 92.45 0.44 10.89

CIFAR10 - WideResNets (WRN-16)

CIFAR10 WRN-16 Params Plot CIFAR10 WRN-16 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_wrn16_1 91.11 0.18 27.06
cifar_wrn16_2 93.40 0.69 101.86
cifar_wrn16_4 94.29 2.75 394.06
cifar_wrn16_6 94.60 6.17 877.10
cifar_wrn16_8 95.05 10.96 1550.99
cifar_wrn16_10 95.03 17.12 2415.71
cifar_wrn16_12 95.11 24.64 3471.28
cifar_wrn16_1_bsconvu 89.09 0.03 5.57
cifar_wrn16_2_bsconvu 91.83 0.10 18.74
cifar_wrn16_4_bsconvu 93.56 0.36 66.59
cifar_wrn16_6_bsconvu 94.13 0.80 143.80
cifar_wrn16_8_bsconvu 94.46 1.41 250.38
cifar_wrn16_10_bsconvu 94.54 2.19 386.31
cifar_wrn16_12_bsconvu 94.82 3.13 551.60
cifar_wrn16_1_bsconvs_p1d4 87.34 0.02 4.01
cifar_wrn16_2_bsconvs_p1d4 91.56 0.06 11.85
cifar_wrn16_4_bsconvs_p1d4 93.31 0.21 38.00
cifar_wrn16_6_bsconvs_p1d4 94.48 0.46 78.84
cifar_wrn16_8_bsconvs_p1d4 94.93 0.80 134.35
cifar_wrn16_10_bsconvs_p1d4 95.17 1.23 204.55
cifar_wrn16_12_bsconvs_p1d4 95.28 1.75 289.42

CIFAR10 - WideResNets (WRN-28)

CIFAR10 WRN-28 Params Plot CIFAR10 WRN-28 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_wrn28_1 92.36 0.37 55.72
cifar_wrn28_2 94.29 1.47 215.79
cifar_wrn28_3 94.99 3.29 479.94
cifar_wrn28_4 94.96 5.85 848.42
cifar_wrn28_6 95.35 13.14 1898.38
cifar_wrn28_8 95.73 23.35 3365.68
cifar_wrn28_10 95.72 36.48 5250.31
cifar_wrn28_12 95.54 52.52 7552.27
cifar_wrn28_1_bsconvu 91.28 0.05 10.09
cifar_wrn28_2_bsconvu 93.39 0.19 34.08
cifar_wrn28_3_bsconvu 93.77 0.42 71.44
cifar_wrn28_4_bsconvu 94.59 0.73 122.43
cifar_wrn28_6_bsconvu 95.16 1.61 265.31
cifar_wrn28_8_bsconvu 95.21 2.82 462.71
cifar_wrn28_10_bsconvu 95.36 4.39 714.64
cifar_wrn28_12_bsconvu 95.46 6.29 1021.10
cifar_wrn28_1_bsconvs_p1d4 90.22 0.04 7.25
cifar_wrn28_2_bsconvs_p1d4 93.13 0.12 21.47
cifar_wrn28_3_bsconvs_p1d4 94.28 0.24 42.23
cifar_wrn28_4_bsconvs_p1d4 94.81 0.41 69.82
cifar_wrn28_6_bsconvs_p1d4 95.10 0.88 145.44
cifar_wrn28_8_bsconvs_p1d4 95.44 1.53 248.32
cifar_wrn28_10_bsconvs_p1d4 96.02 2.36 378.46
cifar_wrn28_12_bsconvs_p1d4 96.29 3.37 535.87

CIFAR10 - WideResNets (WRN-40)

CIFAR10 WRN-40 Params Plot CIFAR10 WRN-40 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_wrn40_1 93.30 0.56 84.37
cifar_wrn40_2 94.44 2.24 329.73
cifar_wrn40_3 95.03 5.04 735.78
cifar_wrn40_4 95.36 8.95 1302.78
cifar_wrn40_6 95.63 20.12 2919.66
cifar_wrn40_8 95.58 35.75 5180.37
cifar_wrn40_10 95.66 55.84 8084.90
cifar_wrn40_1_bsconvu 92.07 0.08 14.61
cifar_wrn40_2_bsconvu 93.91 0.29 49.41
cifar_wrn40_3_bsconvu 94.65 0.63 103.88
cifar_wrn40_4_bsconvu 94.80 1.09 178.27
cifar_wrn40_6_bsconvu 95.20 2.41 386.81
cifar_wrn40_8_bsconvu 95.54 4.24 675.05
cifar_wrn40_10_bsconvu 95.83 6.59 1042.98
cifar_wrn40_1_bsconvs_p1d4 91.24 0.05 10.49
cifar_wrn40_2_bsconvs_p1d4 93.55 0.17 31.08
cifar_wrn40_3_bsconvs_p1d4 94.64 0.36 61.38
cifar_wrn40_4_bsconvs_p1d4 94.98 0.61 101.64
cifar_wrn40_6_bsconvs_p1d4 95.66 1.31 212.04
cifar_wrn40_8_bsconvs_p1d4 95.74 2.27 362.29
cifar_wrn40_10_bsconvs_p1d4 96.00 3.50 552.38

CIFAR100 - MobileNetV1

CIFAR100 MobileNetV1 Params Plot CIFAR100 MobileNetV1 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv1_w1 74.58 3.31 179.43
cifar_mobilenetv1_w3d4 73.48 1.89 102.72
cifar_mobilenetv1_w1d2 71.61 0.87 47.25
cifar_mobilenetv1_w1d4 68.23 0.24 13.01
cifar_mobilenetv1_w1_bsconvu 75.80 3.31 254.73
cifar_mobilenetv1_w3d4_bsconvu 75.27 1.89 145.04
cifar_mobilenetv1_w1d2_bsconvu 73.59 0.87 66.03
cifar_mobilenetv1_w1d4_bsconvu 70.37 0.24 17.68

CIFAR100 - MobileNetV2

CIFAR100 MobileNetV2 Params Plot CIFAR100 MobileNetV2 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv2_w1 74.67 2.35 92.51
cifar_mobilenetv2_w3d4 74.57 1.48 55.24
cifar_mobilenetv2_w1d2 73.03 0.81 27.43
cifar_mobilenetv2_w1d4 67.89 0.36 9.08
cifar_mobilenetv2_w1_bsconvs_p1d6 76.91 2.35 92.51
cifar_mobilenetv2_w3d4_bsconvs_p1d6 75.45 1.48 55.24
cifar_mobilenetv2_w1d2_bsconvs_p1d6 73.43 0.81 27.43
cifar_mobilenetv2_w1d4_bsconvs_p1d6 69.06 0.36 9.08

CIFAR100 - MobileNetV3-small

CIFAR100 MobileNetV3-small Params Plot CIFAR100 MobileNetV3-small FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv3_small_w1 72.93 1.15 18.54
cifar_mobilenetv3_small_w3d4 70.87 0.77 11.46
cifar_mobilenetv3_small_w1d2 66.83 0.49 6.05
cifar_mobilenetv3_small_w7d20 63.16 0.37 3.50
cifar_mobilenetv3_small_w1_bsconvs_p1d6 73.93 1.15 18.54
cifar_mobilenetv3_small_w3d4_bsconvs_p1d6 72.28 0.77 11.46
cifar_mobilenetv3_small_w1d2_bsconvs_p1d6 68.92 0.49 6.05
cifar_mobilenetv3_small_w7d20_bsconvs_p1d6 65.90 0.37 3.50

CIFAR100 - MobileNetV3-large

CIFAR100 MobileNetV3-large Params Plot CIFAR100 MobileNetV3-large FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_mobilenetv3_large_w1 75.09 3.07 68.54
cifar_mobilenetv3_large_w3d4 74.42 1.82 40.75
cifar_mobilenetv3_large_w1d2 71.83 0.91 20.09
cifar_mobilenetv3_large_w7d20 70.34 0.52 10.98
cifar_mobilenetv3_large_w1_bsconvs_p1d6 78.11 3.07 68.54
cifar_mobilenetv3_large_w3d4_bsconvs_p1d6 76.41 1.82 40.75
cifar_mobilenetv3_large_w1d2_bsconvs_p1d6 75.22 0.91 20.09
cifar_mobilenetv3_large_w7d20_bsconvs_p1d6 72.31 0.52 10.98

CIFAR100 - ResNets

CIFAR100 ResNet Params Plot CIFAR100 ResNet FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_resnet20 68.59 0.28 41.42
cifar_resnet56 71.31 0.86 127.39
cifar_resnet110 71.29 1.74 256.34
cifar_resnet302 72.22 4.85 714.83
cifar_resnet602 71.22 9.71 1431.22
cifar_resnet20_bsconvu 64.41 0.05 7.86
cifar_resnet56_bsconvu 69.43 0.13 21.42
cifar_resnet110_bsconvu 71.16 0.24 41.77
cifar_resnet302_bsconvu 72.67 0.67 114.12
cifar_resnet602_bsconvu 73.48 1.33 227.17
cifar_resnet20_bsconvs_p1d4 62.03 0.03 5.66
cifar_resnet56_bsconvs_p1d4 68.72 0.08 15.37
cifar_resnet110_bsconvs_p1d4 71.15 0.16 29.93
cifar_resnet302_bsconvs_p1d4 72.53 0.43 81.70
cifar_resnet602_bsconvs_p1d4 73.05 0.85 162.60

CIFAR100 - WideResNets (WRN-16)

CIFAR100 WRN-16 Params Plot CIFAR100 WRN-16 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_wrn16_1 66.81 0.18 27.07
cifar_wrn16_2 71.29 0.70 101.87
cifar_wrn16_4 75.07 2.77 394.08
cifar_wrn16_6 76.50 6.21 877.14
cifar_wrn16_8 77.30 11.01 1551.03
cifar_wrn16_10 77.28 17.17 2415.77
cifar_wrn16_12 78.02 24.71 3471.34
cifar_wrn16_1_bsconvu 62.79 0.03 5.57
cifar_wrn16_2_bsconvu 68.33 0.11 18.75
cifar_wrn16_4_bsconvu 72.51 0.39 66.62
cifar_wrn16_6_bsconvu 74.02 0.84 143.84
cifar_wrn16_8_bsconvu 75.61 1.45 250.42
cifar_wrn16_10_bsconvu 76.23 2.24 386.36
cifar_wrn16_12_bsconvu 76.48 3.20 551.67
cifar_wrn16_1_bsconvs_p1d4 58.48 0.02 4.02
cifar_wrn16_2_bsconvs_p1d4 68.62 0.07 11.86
cifar_wrn16_4_bsconvs_p1d4 73.01 0.24 38.03
cifar_wrn16_6_bsconvs_p1d4 75.46 0.49 78.87
cifar_wrn16_8_bsconvs_p1d4 77.18 0.84 134.40
cifar_wrn16_10_bsconvs_p1d4 77.64 1.29 204.60
cifar_wrn16_12_bsconvs_p1d4 78.39 1.82 289.49

CIFAR100 - WideResNets (WRN-28)

CIFAR100 WRN-28 Params Plot CIFAR100 WRN-28 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_wrn28_1 69.00 0.38 55.72
cifar_wrn28_2 73.38 1.48 215.80
cifar_wrn28_3 75.25 3.31 479.96
cifar_wrn28_4 76.85 5.87 848.44
cifar_wrn28_6 78.18 13.18 1898.42
cifar_wrn28_8 78.07 23.40 3365.72
cifar_wrn28_10 78.58 36.54 5250.36
cifar_wrn28_12 79.04 52.59 7552.34
cifar_wrn28_1_bsconvu 66.21 0.06 10.09
cifar_wrn28_2_bsconvu 71.78 0.20 34.09
cifar_wrn28_3_bsconvu 73.79 0.44 71.46
cifar_wrn28_4_bsconvu 75.29 0.75 122.45
cifar_wrn28_6_bsconvu 76.67 1.64 265.34
cifar_wrn28_8_bsconvu 77.15 2.87 462.76
cifar_wrn28_10_bsconvu 78.04 4.44 714.70
cifar_wrn28_12_bsconvu 78.30 6.36 1021.17
cifar_wrn28_1_bsconvs_p1d4 64.65 0.04 7.26
cifar_wrn28_2_bsconvs_p1d4 71.55 0.13 21.48
cifar_wrn28_3_bsconvs_p1d4 74.42 0.26 42.25
cifar_wrn28_4_bsconvs_p1d4 76.22 0.43 69.84
cifar_wrn28_6_bsconvs_p1d4 78.18 0.92 145.47
cifar_wrn28_8_bsconvs_p1d4 79.49 1.58 248.36
cifar_wrn28_10_bsconvs_p1d4 80.09 2.42 378.52
cifar_wrn28_12_bsconvs_p1d4 80.26 3.44 535.94

CIFAR100 - WideResNets (WRN-40)

CIFAR100 WRN-40 Params Plot CIFAR100 WRN-40 FLOPs Plot

Model Accuracy (top-1) Params [M] FLOPs [M]
cifar_wrn40_1 70.34 0.57 84.38
cifar_wrn40_2 74.13 2.26 329.74
cifar_wrn40_3 75.70 5.06 735.79
cifar_wrn40_4 77.55 8.97 1302.81
cifar_wrn40_6 77.41 20.15 2919.70
cifar_wrn40_8 78.33 35.79 5180.42
cifar_wrn40_10 78.49 55.90 8084.96
cifar_wrn40_1_bsconvu 68.98 0.09 14.61
cifar_wrn40_2_bsconvu 72.41 0.30 49.42
cifar_wrn40_3_bsconvu 74.91 0.64 103.90
cifar_wrn40_4_bsconvu 76.42 1.12 178.29
cifar_wrn40_6_bsconvu 77.12 2.44 386.85
cifar_wrn40_8_bsconvu 78.01 4.29 675.09
cifar_wrn40_10_bsconvu 78.45 6.64 1043.03
cifar_wrn40_1_bsconvs_p1d4 67.66 0.06 10.49
cifar_wrn40_2_bsconvs_p1d4 73.19 0.18 31.09
cifar_wrn40_3_bsconvs_p1d4 75.83 0.37 61.40
cifar_wrn40_4_bsconvs_p1d4 76.97 0.63 101.66
cifar_wrn40_6_bsconvs_p1d4 78.42 1.34 212.07
cifar_wrn40_8_bsconvs_p1d4 79.51 2.32 362.33
cifar_wrn40_10_bsconvs_p1d4 80.21 3.56 552.44

Requirements

  • Python>=3.6
  • PyTorch>=1.0.0 (support for other frameworks will be added later)

Installation

pip install --upgrade bsconv

Usage

Demo GIF

See here for PyTorch usage details.

Support for other frameworks will be added later.

Please note that the code provided here is work-in-progress. Therefore, some features may be missing or may change between versions.

Change Log

0.4.0 (2020-09-22)

  • BSConv for PyTorch:
    • added support for more model architectures (see bsconv.pytorch.get_model)
    • added result tables and plots for ResNets, WRNs, MobileNets on CIFAR datasets
    • removed script bin/bsconv_pytorch_list_architectures.py, because bsconv.pytorch.get_model is more flexible now (see the BSConv PyTorch usage readme for available architectures)

0.3.0 (2020-06-16)

  • BSConv for PyTorch:
    • added ready-to-use model definitions (MobileNetV1, MobileNetV2, MobileNetsV3, ResNets and WRNs and their BSConv variants for CIFAR and ImageNet/fine-grained datasets)
    • added training script for CIFAR and ImageNet/fine-grained datasets
    • added class for the StanfordDogs dataset

0.2.0 (2020-04-16)

  • BSConv for PyTorch:
    • removed activation and added option for normalization of PW layers in BSConv-S (issue #1) (API change)
    • added option for normalization of PW layers in BSConv-U (API change)
    • ensure that BSConv-S never uses more mid channels (= M') than input channels (M) and added parameter min_mid_channels (= M'_min) (API change)
    • added model profiler for parameter and FLOP counting
    • replacer now shows number of old and new model parameters

0.1.0 (2020-04-08)

  • first public version
  • BSConv for PyTorch:
    • modules BSConvU and BSConvS
    • replacers BSConvU_Replacer and BSConvS_Replacer

Citation

If you find this work useful in your own research, please cite the paper as:

@InProceedings{Haase_2020_CVPR,
    author = {Haase, Daniel and Amthor, Manuel},
    title = {Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved {MobileNets}},
    booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}
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