Fast R-CNN
Train object detection from images by adapting pre-trained classification models on arbitrarily sized regions of interest using ROI pooling.
- Language(s): BrainScript
- Type: Recipe, Tutorial
The Microsoft Cognitive Toolkit—previously known as CNTK—empowers you to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms you already use. Hear about the team that developed the Cognitive Toolkit, or read more below.
The Microsoft Cognitive Toolkit trains and evaluates deep learning algorithms faster than other available toolkits, scaling efficiently in a range of environments—from a CPU, to GPUs, to multiple machines—while maintaining accuracy.
The Microsoft Cognitive Toolkit is built with sophisticated algorithms and production readers to work reliably with massive datasets. Skype, Cortana, Bing, Xbox, and industry-leading data scientists already use the Microsoft Cognitive Toolkit to develop commercial-grade AI.
The Microsoft Cognitive Toolkit offers the most expressive, easy-to-use architecture available. Working with the languages and networks you know, like C++ and Python, it empowers you to customize any of the built-in training algorithms, or use your own.
To help get you started, we’ve assembled 48 different code samples, recipes and tutorials across scenarios working with a variety of datasets: images, numeric, speech and text.
Train object detection from images by adapting pre-trained classification models on arbitrarily sized regions of interest using ROI pooling.
Sequence-to-sequence model with attention mechanism for a grapheme to phoneme translation task on the CMUDict dataset.
Deep residual learning invented by Microsoft Research. This was the winning model of the ILSVRC and MS-COCO challenges in 2015.