# 0.3.0 (2021-28-09) ONNX for inference and transfer learning and ONNX Model Hub

Features:

• Implemented the copying for the Functional and Sequential models
• Implemented the copying for the TensorFlow-based Inference Model
• Implemented the experimental ONNX integration:
  • added new 'onnx' module
  • added the ONNXModel implementing the common InferenceModel interface
  • ONNX model could be used as a preprocessing stage for the TensorFlow model
  • prepared ONNX model without top layers could be fine-tuned via training of top layers implemented with TensorFlow-based layers
• Added SSD and YOLOv4 object detection models to the Model Hub
• Added Fan2D106 face alignment model to the Model Hub
• Added SSDObjectDetectionModel with the easy API for object detection, including pre- and post-processing
• Added a few models in ONNX format to the Model Hub
  • ResNet18
  • ResNet34
  • ResNet50
  • ResNet101
  • ResNet152
  • ResNet18V2
  • ResNet34V2
  • ResNet50V2
  • ResNet101V2
  • ResNet152V2
  • EfficientNetV4
• Added new TensorFlow-based models to the Model Zoo (or Model Hub):
  • NasNetMobile
  • NasNetLarge
  • DenseNet121
  • DenseNet169
  • DenseNet201
  • Xception
• Added ResNet18 and ResNet34 TensorFlow-based models to ModelZoo
• Added L1 and L2 regularization to the layers
• Added Identity initializer
• Added Orthogonal initializer
• Added Softmax activation layer
• Added LeakyReLU activation layer
• Added PReLU activation layer
• Added ELU activation layer
• Added ThresholdedReLU activation layer
• Added Conv1D layer
• Added MaxPooling1D layer
• Added AveragePooling1D layer
• Added GlobalMaxPooling1D layer
• Added GlobalAveragePooling1D layer
• Added Conv3D layer
• Added MaxPooling3D layer
• Added AveragePooling3D layer
• Added GlobalAveragePooling3D layer
• Added GlobalMaxPool2D layer
• Added GlobalMaxPool3D layer
• Added Cropping1D and Cropping3D layers
• Added Permute layer
• Added RepeatVector layer
• Added UpSampling1D, UpSampling2D and UpSampling3D layers
• Added Gelu activation function
• Added HardShrink activation function
• Added LiSHT activation function
• Added Mish activation function
• Added Snake activation function
• Added Tanh shrink activation function
• Added TimeStopping callback

Bugs:

• Added missed loaders for the ReLU and ELU activation layers
• Add model export for a few layers (Concatenate, DepthwiseConv2D, SeparableConv2D) missed in ModelSaver.kt
• Fixed the use-case when ModelSaver fails on saving Input with 2d and 3d tensors
• Fixed a StackOverflowError in objectDetectionSSD.kt example
• Fixed a problem with the confusing logs during weights loading from .h5 file
• Fixed the Windows separator usage instead of File.separator in the Save and Load preprocessors
• Fixed the incorrect temporary folder for the cat-vs-dogs dataset
• Fixed the problem when ImageConverter and Loading do not close open streams
• Fixed the Image Preprocessing DSL issues
• Reduced time complexity of FloatArray::argmax to linear

API breaking changes:

• Renamed ModelZoo to the ModelHub
• Changed the ImagePreprocessing DSL: loading and saving are moved to the separate level of DSL
• Changed the TrainableModel::summary API to return ModelSummary

Infrastructure:

• Loaded the weights and JSON configurations of the newly added ModelHub models to S3 storage
• Moved ImageDSL and Dataset API to the separate 'dataset' module
• Added a new 'visualization' module with the basic support for painting on Swing and in Jupyter Notebook with lets-plot
• Transformed the project from the single-module project to the multi-module project

Docs:

• Created website with API Documentation from KDoc via Dokka
• Added support for the multiple version API Documentation from KDoc via Dokka
• Updated all existing tutorials
• Updated the Readme.md
• Updated the existing KDocs
• Added a new tutorial about ONNX models usage
• Added a new tutorial about Transfer Learning with ONNX ResNet no-top model and TensorFlow

Examples:

• Added an example of SSDObjectDetectionModel usage and visualization of the detected objects on the Swing panel
• Added an example of Fan2D106 (face alignment) model and landmarks visualization on the Swing panel
• Added an example where the prepared ONNX model without top layers is fine-tuned via training of top layers implemented with TensorFlow-based layers
• Added a lot of examples for the newly added to the ModelHub models (ONNX-based and TensorFlow-based)
• Added an example with the model SoundNet trained on Free Spoken Digits Dataset to classify the audio
• Updated 'visualization' examples with the new Batik and lets-plot support

Tests:

• Added tests for ModelLoading
• Added tests for InputLayer
• Added tests for all newly added layers

Thanks to our contributors:

• Alexey Zinoviev (@zaleslaw)
• Julia Beliaeva (@juliabeliaeva)
• Masoud Kazemi (@mkaze)
• Ansh Tyagi (@therealansh)
• Maciej Procyk (@avan1235)
• Veniamin Viflyantsev (@knok16)
• Stan van der Bend (@dosier)
• Axel Pahl (@apahl)
• @cagriyildirimR
• @d-lowl
• Hauke Brammer (@hbrammer)
• Xa9aX ツ (@digantamisra98)
• Sergey Kokorin (@kokorins)
• Femi Alaka (@femialaka)