MyCaffe 0.11.2.9-beta1

A complete C# re-write of Berkeley's open source Convolutional Architecture for Fast Feature Encoding (CAFFE) for Windows C# Developers with full On-line Help, now with Single-Shot MultiBox, TripletNet, SiameseNet, NoisyNet, Deep Q-Network and Policy Gradient Reinforcement Learning, cuDNN LSTM Recurrent Learning, and Neural Style Transfer support!

This is a prerelease version of MyCaffe.
Install-Package MyCaffe -Version 0.11.2.9-beta1
dotnet add package MyCaffe --version 0.11.2.9-beta1
<PackageReference Include="MyCaffe" Version="0.11.2.9-beta1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MyCaffe --version 0.11.2.9-beta1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: MyCaffe, 0.11.2.9-beta1"
#r directive can be used in F# Interactive, C# scripting and .NET Interactive. Copy this into the interactive tool or source code of the script to reference the package.
// Install MyCaffe as a Cake Addin
#addin nuget:?package=MyCaffe&version=0.11.2.9-beta1&prerelease

// Install MyCaffe as a Cake Tool
#tool nuget:?package=MyCaffe&version=0.11.2.9-beta1&prerelease
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

MyCaffe AI Platform and Test Application (CUDA 11.2, cuDNN 8.1) with Single-Shot Multi-Box Object Detection and ONNX AI Model Support (onnx.ai).

CUDA 11.2, cuDNN 8.1, nvapi 460, Windows 10-1909, Driver 461.40

MyCaffe[1] (a complete C# re-write of CAFFE[2]) now supports extra debugging features and Distributed AI!

IMPORTANT NOTE: When using TCC mode, we recommend that ALL headless GPU’s are placed in TCC mode for we have experienced stability issues when using a mix of TCC and WDM modes with headless GPU’s.

REQUIRED SOFTWARE to use MyCaffe:
1.) Download and install full version of Microsoft SQL Express 2016 (or later). NOTE: The full version of SQL Express must installed as opposed to the light version included in Visual Studio. Microsoft SQL Express can be downloaded from https://www.microsoft.com/en-us/sql-server/sql-server-downloads

REQUIRED SOFTWARE to build MyCaffe:
1.) Install NVIDIA CUDA 11.2 which you can download from https://developer.nvidia.com/cuda-downloads
2.) Install NVIDIA cuDNN 8.1 which you can download from https://developer.nvidia.com/cudnn

To download MyCaffe Support Installs for CUDA 11.1, CUDA 11.0 or CUDA 10.2 support, please see the MyCaffe Beta Site

This release of the MyCaffe AI Platform and Test Applications has the following new additions:
• CUDA 11.2/cuDNN 8.1 supported (with driver 461.40 or above).
• Windows 1909, OS Build 18363.1316 now supported.
• Upgraded to Google.Protobuf 3.14
• Added support installs for older CUDA 11.1, CUDA 11.0 and CUDA 10.2
• Added compute and sm support for 3.5 through 8.0.
• Added Clip layer support to Onnx conversion.
• Added new Transpose Layer.
• Added BatchNorm weight import to Onnx conversion.
• Added support for ONNX InceptionV2 conversion to MyCaffe.
• Added background file writer thread support to Database.
• Added SubAbs support to SimpleDatum.
• Added SetPixel support to Blob
• Added SetPixel support to CudaDnnDLL.
• Expanded counts and offsets to long in CudaDnnDLL
• Improved sample dataset load times.
• Improved image load cancel options.
• Improved MNIST dataset loading speed.
• Improved CIFAR-10 dataset loading speed.
• Optimized image loading.

The following bug fixes are in this release:
• Fixed TestConvolutionLayer::TestNDAgainst2D test.
• Fixed TestConvolutionLayer::TestGradient3D test.
• Fixed TestConvolutionLayer::TestGradient3DCuDnn test.
• Fixed TestConvolutionLayer::TestGradient3DCuDnnWithTensor test.
• Fixed TestDeconvolutionLayer::TestNDAgainst2D test.
• Fixed TestDeconvolutionLayer::TestGradient3D test.
• Fixed bug in Convolution::conv_col2im.
• Fixed bug in BlobShape dim not setting.
• Fixed bug in Clip Layer max not setting.
• Fixed bug in exporting to ONNX model files (Conv, Pool, LRN, and Reshape)
• Fixed bug in MyCaffe Test App related to using different versions of CUDA.

Easily run Single-Shot Multi-Box Nets[3][4], import/export ONNX AI Models, run Triplet Nets[5][6], run Siamese Nets[7][8], Neural Style, train Deep Q-Learning or Policy Gradient models to beat Pong or Cart-Pole, or create the CIFAR-10 and MNIST datasets using the MyCaffe Test Application which you can download from the MyCaffe GitHub site.

Schedule distributed AI work packages, or create and train Single-Shot Multi-Box[3][4], Triplet Net[5][6], Siamese Net[7][8], Deep Q-Learning with NoisyNet and Experienced Replay, Policy Gradient, Neural Style Transfer, Recurrent Learning, Policy Gradient Reinforcement Learning, Auto-Encoder, DANN and ResNet models by following step-by-step instructions in the SignalPop Tutorials. And, to see other cool examples that show what MyCaffe can do, see the SignalPop Examples.

If you would like to visually design, develop, test and debug your models, see the SignalPop AI Designer specifically designed to enhance your MyCaffe deep learning.

Also, check out the SignalPop Universal Miner that not only keeps your GPU's cool as you train, but also gives you detailed information on each of your GPU's (such as temperature, fan speed, overclock, and usage), and allows you to easily mine Ethereum. When not training AI, put those GPU's to use making some Ether - never let a good GPU go to waste!

Happy ‘deep’ learning!

[1] MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning by D. Brown, 2018.

[2] Caffe: Convolutional Architecture for Fast Feature Embedding by Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell, 2014, arXiv:1408.5093

[3] SSD: Single Shot MultiBox Detector by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, 2016.

[4] GitHub: SSD: Single Shot MultiBox Detector, by weiliu89/caffe, 2016

[5] Deep metric learning using Triplet network by Elad Hoffer and Nir Ailon, 2018, arXiv:1412.6622

[6] In Defense of the Triplet Loss for Person Re-Identification by Alexander Hermans, Lucas Beyer and Bastian Liebe, 2017, arXiv:1703.07737v2

[7] Siamese Network Training with Caffe by Berkeley Artificial Intelligence (BAIR)

[8] Siamese Neural Network for One-shot Image Recognition by G. Koch, R. Zemel and R. Salakhutdinov, ICML 2015 Deep Learning Workshop, 2015.

MyCaffe AI Platform and Test Application (CUDA 11.2, cuDNN 8.1) with Single-Shot Multi-Box Object Detection and ONNX AI Model Support (onnx.ai).

CUDA 11.2, cuDNN 8.1, nvapi 460, Windows 10-1909, Driver 461.40

MyCaffe[1] (a complete C# re-write of CAFFE[2]) now supports extra debugging features and Distributed AI!

IMPORTANT NOTE: When using TCC mode, we recommend that ALL headless GPU’s are placed in TCC mode for we have experienced stability issues when using a mix of TCC and WDM modes with headless GPU’s.

REQUIRED SOFTWARE to use MyCaffe:
1.) Download and install full version of Microsoft SQL Express 2016 (or later). NOTE: The full version of SQL Express must installed as opposed to the light version included in Visual Studio. Microsoft SQL Express can be downloaded from https://www.microsoft.com/en-us/sql-server/sql-server-downloads

REQUIRED SOFTWARE to build MyCaffe:
1.) Install NVIDIA CUDA 11.2 which you can download from https://developer.nvidia.com/cuda-downloads
2.) Install NVIDIA cuDNN 8.1 which you can download from https://developer.nvidia.com/cudnn

To download MyCaffe Support Installs for CUDA 11.1, CUDA 11.0 or CUDA 10.2 support, please see the MyCaffe Beta Site

This release of the MyCaffe AI Platform and Test Applications has the following new additions:
• CUDA 11.2/cuDNN 8.1 supported (with driver 461.40 or above).
• Windows 1909, OS Build 18363.1316 now supported.
• Upgraded to Google.Protobuf 3.14
• Added support installs for older CUDA 11.1, CUDA 11.0 and CUDA 10.2
• Added compute and sm support for 3.5 through 8.0.
• Added Clip layer support to Onnx conversion.
• Added new Transpose Layer.
• Added BatchNorm weight import to Onnx conversion.
• Added support for ONNX InceptionV2 conversion to MyCaffe.
• Added background file writer thread support to Database.
• Added SubAbs support to SimpleDatum.
• Added SetPixel support to Blob
• Added SetPixel support to CudaDnnDLL.
• Expanded counts and offsets to long in CudaDnnDLL
• Improved sample dataset load times.
• Improved image load cancel options.
• Improved MNIST dataset loading speed.
• Improved CIFAR-10 dataset loading speed.
• Optimized image loading.

The following bug fixes are in this release:
• Fixed TestConvolutionLayer::TestNDAgainst2D test.
• Fixed TestConvolutionLayer::TestGradient3D test.
• Fixed TestConvolutionLayer::TestGradient3DCuDnn test.
• Fixed TestConvolutionLayer::TestGradient3DCuDnnWithTensor test.
• Fixed TestDeconvolutionLayer::TestNDAgainst2D test.
• Fixed TestDeconvolutionLayer::TestGradient3D test.
• Fixed bug in Convolution::conv_col2im.
• Fixed bug in BlobShape dim not setting.
• Fixed bug in Clip Layer max not setting.
• Fixed bug in exporting to ONNX model files (Conv, Pool, LRN, and Reshape)
• Fixed bug in MyCaffe Test App related to using different versions of CUDA.

Easily run Single-Shot Multi-Box Nets[3][4], import/export ONNX AI Models, run Triplet Nets[5][6], run Siamese Nets[7][8], Neural Style, train Deep Q-Learning or Policy Gradient models to beat Pong or Cart-Pole, or create the CIFAR-10 and MNIST datasets using the MyCaffe Test Application which you can download from the MyCaffe GitHub site.

Schedule distributed AI work packages, or create and train Single-Shot Multi-Box[3][4], Triplet Net[5][6], Siamese Net[7][8], Deep Q-Learning with NoisyNet and Experienced Replay, Policy Gradient, Neural Style Transfer, Recurrent Learning, Policy Gradient Reinforcement Learning, Auto-Encoder, DANN and ResNet models by following step-by-step instructions in the SignalPop Tutorials. And, to see other cool examples that show what MyCaffe can do, see the SignalPop Examples.

If you would like to visually design, develop, test and debug your models, see the SignalPop AI Designer specifically designed to enhance your MyCaffe deep learning.

Also, check out the SignalPop Universal Miner that not only keeps your GPU's cool as you train, but also gives you detailed information on each of your GPU's (such as temperature, fan speed, overclock, and usage), and allows you to easily mine Ethereum. When not training AI, put those GPU's to use making some Ether - never let a good GPU go to waste!

Happy ‘deep’ learning!

[1] MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning by D. Brown, 2018.

[2] Caffe: Convolutional Architecture for Fast Feature Embedding by Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell, 2014, arXiv:1408.5093

[3] SSD: Single Shot MultiBox Detector by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, 2016.

[4] GitHub: SSD: Single Shot MultiBox Detector, by weiliu89/caffe, 2016

[5] Deep metric learning using Triplet network by Elad Hoffer and Nir Ailon, 2018, arXiv:1412.6622

[6] In Defense of the Triplet Loss for Person Re-Identification by Alexander Hermans, Lucas Beyer and Bastian Liebe, 2017, arXiv:1703.07737v2

[7] Siamese Network Training with Caffe by Berkeley Artificial Intelligence (BAIR)

[8] Siamese Neural Network for One-shot Image Recognition by G. Koch, R. Zemel and R. Salakhutdinov, ICML 2015 Deep Learning Workshop, 2015.

Release Notes

MyCaffe AI Platform

NuGet packages

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Version History

Version Downloads Last updated
0.11.2.9-beta1 78 2/3/2021
0.11.1.132-beta1 151 11/21/2020
0.11.1.56-beta1 170 10/17/2020
0.11.0.188-beta1 194 9/24/2020
0.11.0.65-beta1 252 8/6/2020
0.10.2.309-beta1 320 5/31/2020
0.10.2.124-beta1 255 1/21/2020
0.10.2.38-beta1 236 11/29/2019
0.10.1.283-beta1 244 10/28/2019
0.10.1.221-beta1 241 9/17/2019
0.10.1.169-beta1 353 7/8/2019
0.10.1.145-beta1 363 5/31/2019
0.10.1.48-beta1 374 4/18/2019
0.10.1.21-beta1 357 3/5/2019
0.10.0.190-beta1 501 1/15/2019
0.10.0.140-beta1 446 11/29/2018
0.10.0.122-beta1 469 11/15/2018
0.10.0.75-beta1 474 10/7/2018