v0.4.0 (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)

v0.3.0 (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

v0.2.0 (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

v0.1.0 (2020-04-08)

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