xoolive/caffe

Caffe: a Fast framework for deep learning. Custom version with built-in sparse inputs, segmentation, object detection, class weights, and custom layers

Caffe for DeepDetect

This is a slightly modified version of Caffe as used by the Deep Learning API & server Deepdetect. The repository is kept up to date with the original Caffe master branch.

Improvements and new features include:

• Switch from LOG(FATAL) error to CaffeErrorException thrown on every recoverable errors. This allows the safe use of Caffe as a C++ library from external applications, and in production
• Various fixes, including ability to run the exact same job in parallel
• Makefile fixes with default build supporting all NVIDIA architectures
• Sparse inputs and CPU/GPU computations
• Support for class weights applied to Softmax loss, useful for training over imbalanced datasets
• SSD: Single Shot MultiBox Detector for object detection in images
• Support for lightweight nets via accelerated depthwise convolutions (BVLC#5665) and shufflenet layer (https://github.com/farmingyard/ShuffleNet).
• Support for image segmentation, via PSPNet, U-Net, SegNet, etc...
• Support for Squeeze & Excitation Nets (https://github.com/hujie-frank/SENet).
• Support for SoftNMS with SSD (https://arxiv.org/abs/1704.04503)
• Support for Focal-Loss with SSD (https://arxiv.org/abs/1708.02002)
• Support for AMSGrad (fix to Adam optimizer, https://openreview.net/forum?id=ryQu7f-RZ)
• Support for Connectionist Temporal Classification (CTC) from https://github.com/baidu-research/warp-ctc and https://github.com/xmfbit/warpctc-caffe
• Support for SGDR (http://arxiv.org/abs/1608.03983)
• Support for RefineDet object detection (https://arxiv.org/abs/1711.06897)

While this is intended to be used with DeepDetect, this is a great alternative to the original Caffe if you'd like to avoid uncaptured errors, train from text or sparse data, need built-in image detection.