EBLearn

Categories: Coding & Developer Tools, Data Analysis | Pricing: Free | Official Website ↗

EBLearn is an open-source C++ library for machine learning, providing tools for building, training, and running convolutional networks.

EBLearn is an object-oriented C++ library that implements various machine learning models, including energy-based learning and gradient-based learning for machines composed of multiple heterogeneous modules. It offers a complete set of tools for developing, training, and deploying convolutional networks. The library is self-contained, with no external dependencies for its core functionalities. It includes CPU optimizations like Intel IPP, SSE, and OpenMP support, as well as OpenMPI cluster support for training and detection. EBLearn has been used to achieve state-of-the-art results in tasks such as pedestrian detection, road sign classification, and house number classification. EBLearn is structured into three main sections: libidx (a tensor library), libeblearn (implementing machine learning models), and tools (helper libraries and binaries including a GUI, dataset compiler, and binaries for training and real-time detection).

Key Features

Object-oriented C++ library
Energy-based learning implementation
Gradient-based learning implementation
Tools for convolutional networks
Tensor library (libidx)
Dataset compiler
Training binary (train)
Detection binary (detect)

Pros

Open-source and free to use
Allows building complex classifiers without extensive coding
Self-contained with no external core dependencies
Includes CPU optimizations and cluster support
Achieved state-of-the-art results in various tasks

Cons

Development appears to have ceased (last news update 2013)
Documentation might be outdated
Community support may be limited due to age
Requires C++ programming knowledge for advanced use
No clear roadmap for future development

Use Cases

Building custom convolutional neural networks
Developing face detection systems
Implementing pedestrian detection
Classifying road signs
Analyzing house numbers from street view images

Best For

Researchers in machine learning
Developers building custom ML applications in C++
Academics studying energy-based and gradient-based learning
Organizations needing a self-contained ML library

Integrations: Matlab I/O wrappers

Platforms: Windows, Linux, macOS, Android, iOS

Watch demo on YouTube ↗

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