Check out the [classification demo](http://demo.caffe.berkeleyvision.org/)!
-<!-- BVLC hosts a quick [classification demo](http://demo.caffe.berkeleyvision.org/) using Caffe. -->
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## Why Caffe?
Caffe aims to provide computer vision scientists and practitioners with a **clean and modifiable implementation** of state-of-the-art deep learning algorithms.
* [Image Classification \[notebook\]][imagenet_classification]: classify images with the pretrained ImageNet model by the Python interface.
* [Detection \[notebook\]][detection]: run a pretrained model as a detector in Python.
* [Visualizing Features and Filters \[notebook\]][visualizing_filters]: extracting features and visualizing trained filters with an example image, viewed layer-by-layer.
+* [Editing Model Parameters \[notebook\]][net_surgery]: how to do net surgery and manually change model parameters.
* [LeNet / MNIST Demo](/mnist.html): end-to-end training and testing of LeNet on MNIST.
* [CIFAR-10 Demo](/cifar10.html): training and testing on the CIFAR-10 data.
* [Training ImageNet](/imagenet_training.html): recipe for end-to-end training of an ImageNet classifier.
[imagenet_classification]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/imagenet_classification.ipynb
[detection]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb
[visualizing_filters]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb
+[net_surgery]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/net_surgery.ipynb
## Citing Caffe