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| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| api-0.3.0-all.jar | 2021-09-24 | 150.8 MB | |
| api-0.3.0.jar | 2021-09-24 | 792.7 kB | |
| # 0.3.0 (2021-28-09) ONNX for inference and transfer learning and ONNX Model Hub source code.tar.gz | 2021-09-24 | 73.1 MB | |
| # 0.3.0 (2021-28-09) ONNX for inference and transfer learning and ONNX Model Hub source code.zip | 2021-09-24 | 73.5 MB | |
| README.md | 2021-09-24 | 8.2 kB | |
| Totals: 5 Items | 298.2 MB | 0 | |
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)