Deep-learning
Out-of-the-Box
PyTorch integration for common deep learning scenarios
PyTorch integration for common deep learning scenarios
Smarter than grid-search with SigOpt
Run your own, and reproduce the experiments of others in the cloud
An image is worth a thousand lines of logs
Scripts to prepare your datasets faster
How effective are your changes?
Find out what happens inside your network
Understand your networks better on toy-problems
Abstract.We introduce DeepDIVA: an infrastructure designed to enable and intuitive setup of reproducible experiments with a range of useful analysis functionality. Reproducing scientific can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given with a very limited amount of information or share their own experiments with others. Moreover, the framework a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source, and accessible as Web Service through DIVAServices.
Paper onarXiv.org.
Abstract.The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We present DeepDIVA, a framework designed to facilitate easy experimentation and their reproduction. This framework allows researchers to share their experiments with others, while providing functionality that allows for easy experimentation, such as: boilerplate code, experiment management, hyper-parameter optimization, verification of data integrity and visualization of data and results. Additionally, the code of DeepDIVA is well-documented and supported by several tutorials that allow a new user to quickly familiarize themselves with the framework.