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Release notes

All notable changes to this project will be documented in this file.

v1.1.0

New major release. Added support for Keras 3

v1.0.7

Added convNeXt 3D models

v1.0.6

Padding fixes for mobilenetv2, inceptionresnetv2 and inceptionv3. It's needed for correct work with segmentation models.

v1.0.4

  • Added EfficientNet and EfficientNet v2 models (with converted imagenet weights)
  • Fixed error in converter which skip bias for imagenet weights
  • include_top parameter now False by default. It was made because imageNet weights now only available for include_top = False. If use include_top=True and classes=<N>, you must use weights=None.
  • New converted imagenet weights available
  • Added parameter stride_size to control how strides/poolings are made. By default it's equal to 2. Now it's possible to set individual stride for each stage of model. For example:
stride_size = [
    (1, 2, 1),
    (2, 2, 4),
    (2, 2, 4),
    (2, 4, 2),
    (2, 2, 2),
]

Here each tuple control individual stride/pooling. While each tuple control stride for each dimension. Strides doesn't affect model structure and you can use imagenet weights with such modified models.

  • For some models (resnet, resnext, senet, densenet, vgg16, vgg19) it's possible to increase number of blocks using repetition parameter. It can be useful if you need to add more poolings and layers. imagenet weights won't work for modified models.
  • Minimum TF version bumped to 2.8.0