DIY Deep Learning for Vision- a Hands-On Tutorial with Caffe

Examples:

Logistic Regression (in Python): http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/hdf5_classification.ipynb

Learn LeNet on MNIST: http://caffe.berkeleyvision.org/gathered/examples/mnist.html

Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data

http://tutorial.caffe.berkeleyvision.org/gathered/examples/finetune_flickr_style.html

Caffe Demo : http://demo.caffe.berkeleyvision.org/

Feature Visualization : http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/filter_visualization.ipynb

How to transform models in Caffe:

http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/net_surgery.ipynb

Related projects:

R-CNN: Regions with CNN

Ross Girshick et al.Rich feature hierarchies for accurate object detection and semanticsegmentation. CVPR14.

http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/detection.ipynb

Full scripts:

https://github.com/rbgirshick/rcnn

Visual Style Recognition:

Karayev et al.Recognizing Image Style. BMVC14. Caffe fine-tuning example

Demo: http://demo.vislab.berkeleyvision.org/

Latest Roast:

Model Zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo

- BVLC reference models

- VGG Devil + ILSVRC14 models in the zoo

- Network-in-Network / CCCP model in the zoo

Caffe + cuDNN

Parallel / distributed training across CPUs, GPUs, and cluster nodes

https://github.com/BVLC/caffe/pull/1148

参考：DIY Deep Learning for Vision with Caffe slides

https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/preview?sle=true#slide=id.p