- Introduction
- Setup
- Quick Mode
- Update Mode
- Expert Mode
- Hyper Parameter Analyser
- Compare Experiments
- Resume Training
- Load foldered dataset
- Set number of epochs
- Run training
ptf = prototype(verbose=1)
ptf.Prototype("sample-project-1", "sample-experiment-1")
ptf.Default(dataset_path="./dataset_cats_dogs_train/",
model_name="resnet18", freeze_base_network=True, num_epochs=2)
ptf.Train()
img_name = "./monk/datasets/test/0.jpg";
predictions = ptf.Infer(img_name=img_name, return_raw=True);
print(predictions)
- Add created experiments with different hyperparameters
- Generate comparison plots
ctf = compare(verbose=1);
ctf.Comparison("Sample-Comparison-1");
ctf.Add_Experiment("sample-project-1", "sample-experiment-1");
ctf.Add_Experiment("sample-project-1", "sample-experiment-2");
.
.
.
ctf.Generate_Statistics();
- Tensorflow 2.0
- Chainer
- Intel VINO toolkit
- OpenCV
- Dlib
- Python PIL
- TensorRT Acceleration
- Intel Acceleration
- Echo AI - for Activation functions
- Model Visualization
- Pre-processed data visualization
- Learned feature visualization
- NDimensional data input
- Data input from npy files
- Data input from hdfs files
- Support for Dicom data
- Support for Tiff data
- Multi-label Image Classification
- Custom model development
- Incorporate pep coding standards
- Functional Documentation
- Tackle Multiple versions of libraries
- Add unit-testing
- Contribution guidelines
Copyright 2019 onwards, Tessellate Imaging Private Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.