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dotnet add package MyCaffe --version
NuGet\Install-Package MyCaffe -Version
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<PackageReference Include="MyCaffe" Version="" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MyCaffe --version
#r "nuget: MyCaffe,"
#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=

// Install MyCaffe as a Cake Tool
#tool nuget:?package=MyCaffe&version=

MyCaffe AI Platform (CUDA 11.7.1, cuDNN 8.4.1) version 1.11.7 ready!

MyCaffe version 1 is here! The MyCaffe AI Platform provides an easy AI solution for multiple AI disciplines, including:

• Classification with AlexNet, ResNet, VGG, NoisyNet, and Inception models • Classification with SiameseNet • Classification with TripletNet • Auto Encoders and DANN • Onnx AI Model Support (import and export) • Object detection with Single-Shot Multi Box (SSD) • Reinforcement Learning with Policy Gradient and Deep Q-Learning • Recurrent Learning with CharNet • Neural Style Transfer • Seq2Seq Models

Speed up AI training with the MyCaffe in-memory database that caches full datasets or drip-fed datasets into your local RAM on one side while feeding the training process on the other with label balanced data. Easily Train on multiple-GPUs with NCCL.

CUDA, cuDNN, nvapi 510, Windows 10-21H2/Windows 11-21H2, Driver 516.40/516.59

MyCaffe[1] (a complete C# re-write of CAFFE[2]) now supports Visual Studio 2022 and CUDA 11.7.1/cuDNN 8.4.1 and Windows 11!

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

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 be installed as opposed to the light version included in Visual Studio. Microsoft SQL Express can be downloaded from

This release of the MyCaffe AI Platform and Test Applications has the following new additions: • CUDA 510/driver 516.40/516.59 • Windows 11 21H2 • Windows 10 21H2, OS Build 19044.1865, SDK 10.0.19041.0 • Added MyCaffe.python module with new PythonInterop class. • Converted MAELossLayer into MeanErrorLossLayer with MAE support. • Added ability to enable/disable gradient clipping status. • Added label to one-hot vector conversion to DataLayer. • Added FocusMask support to ImageData. • Added support for multiple ignore labels to AccuracyDecodeLayer. • Added support for ignore label to MyCaffeControl.Run • Added secondary labels detected to MyCaffeControl.TestMany • Upgraded to GoogleProtobuf 3.21.4

The following bug fixes are in this release: • Fixed bug in SimpleDatum.Sub impacting • Fixed bug in MAELossLayer where normalization was incorrect. • Fixed bug in Solver where ‘display’ was not being used. • Fixed Google Protobuf version error.

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 GPUs cool as you train, but also gives you detailed information on each of your GPUs (such as temperature, fan speed, overclock, and usage), and allows you to easily mine Ethereum. When not training AI, put those GPUs 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.

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MyCaffe AI Platform