#Caffe finetune googlenet windows#
NCCL is now required for multi-GPU operationĪs a reminder the OpenCL and Windows branches continue to make progress with the community leadership of Fabian Tschopp and Guillaume Dumont resp.Ī lot has happened since the last release! This packages up ~800 commits by 119 authors.cuDNN compatibility is now at v5 + v4 and cuDNN v3 and earlier are not supported.Crop layer checks only the crop dimensions it should #3993.Exp layer for base e and shift != 0 #3937.
#Caffe finetune googlenet upgrade#
Net upgrade tools catch mixed versions, handle input fields, and log outputs #3755.multi-GPU parallelism through NCCL + multi-GPU python interface #4563.force backprop on or off by propagate_down #3942.expose all NetState options for all-in-one nets #3863.net spec coordinate mapping and cropping for FCNs #3613.Sigmoid Cross Entropy Loss on GPU #4908 and with ignore #4986.Batch Norm docs, numerics, and robust proto def #4704 #5184.Tied weights with transpose for InnerProduct layer #3612.Crop layer for aligning coordinate maps for FCNs #3570.Parameter layer for learning any bottom #2047.See all merged PRs since the last release. With all releases one should do make clean & make superclean to clear out old materials before compiling the new release. We hope to catch any lurking issues, improve documentation, and polish the packaging for then. This is intended to be the last release candidate before 1.0. This packages up 348 commits by 68 authors. It's a new year and a new release candidate. Although Caffe2 is a departure from the development line of Caffe 1.0, we are planning a migration path for models just as we have future-proofed Caffe models in the past. While Caffe 1.0 development will continue with 1.1, Caffe2 is the new framework line for future development led by Yangqing Jia. Now that 1.0 is done, the next generation of the framework- Caffe2-is ready to keep up the progress on DIY deep learning in research and industry. As development is never truly done, there's always 1.1! Stay tuned for the next steps in DIY deep learning with Caffe. As part of 1.0 we will be welcoming collaborators old and new to join as members of the Caffe core. Thanks for all of your efforts leading us to Caffe 1.0! Your part in development, community, feedback, and framework usage brought us here. winner of the ACM MM open source award 2014 and presented as a talk at ICML MLOSS 2015.downloads: 10k+ downloads and updates a month, ~50k unique visitors to the home page every two weeks, and >100k unique downloads of the reference models.development: 10k+ forks, >1 contribution/day on average, and dedicated branches for OpenCL and Windows.community: 250+ contributors, 15k+ subscribers on github, and 7k+ members of the mailing list.industry: adopted by Facebook, NVIDIA, Intel, Sony, Yahoo! Japan, Samsung, Adobe, A9, Siemens, Pinterest, the Embedded Vision Alliance, and more.research: nearly 4,000 citations, usage by award papers at CVPR/ECCV/ICCV, and tutorials at ECCV'14 and CVPR'15.Let's review the progress culminating in our 1.0: This release marks the convergence of development into a stable, reference release of the framework and a shift into maintenance mode.
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