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DIY Deep Learning for Vision- a Hands-On Tutorial with Caffe
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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
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:
Related projects:
R-CNN: Regions with CNN
Ross Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR14.
Full scripts:
Visual Style Recognition:
Karayev et al. Recognizing Image Style. BMVC14. Caffe fine-tuning example
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
参考：DIY Deep Learning for Vision with Caffe slides
https://wap.sciencenet.cn/blog-1583812-844177.html
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