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BrainCaffe-wiki provide basic information to support your project!
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1.Tutorial Documentation : Caffe Tutorial (Kor)
Practical guide and framework reference.
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2.Model Zoo : Caffe Model Zoo (Kor)
BVLC suggests a standard distribution format for Caffe models, and provides trained models.
All of below examples also provided translation for Korean. link for original ones are on translated title beside
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1.ImageNet Tutorial : Brewing ImageNet (Kor)
Train and test "CaffeNet" on ImageNet data.
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2.LeNet MNIST Tutorial : Training LeNet on MNIST with Caffe (Kor)
Train and test "LeNet" on the MNIST handwritten digit data.
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3.CIFAR-10 tutorial : Alex’s CIFAR-10 tutorial, Caffe style (Kor)
Train and test Caffe on CIFAR-10 data.
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4.Fine-tuning for style recognition : Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data (Kor)
Fine-tune the ImageNet-trained CaffeNet on the "Flickr Style" dataset.
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5.Feature extraction with Caffe C++ code : Extracting Features (Kor)
Extract CaffeNet / AlexNet features using the Caffe utility.
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6.CaffeNet C++ Classification example : Classifying ImageNet: using the C++ API (Kor)
A simple example performing image classification using the low-level C++ API.
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7.Web demo : Web Demo (Kor)
Image classification demo running as a Flask web server.
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8.Siamese Network Tutorial : Siamese Network Training with Caffe (Kor)
Train and test a siamese network on MNIST data.
The Analysis of [Pose-v2]'s prototxts will starts soon. it will be attached on wiki too for good example of Caffe Coding.
More information of [Pose-v2], go to project of Structured Feature Learning for Pose Estimation by Xiaogang Wang et al.