Lots of people have used Caffe to train models of different architectures and applied to different problems, ranging from simple regression to AlexNet-alikes to Siamese networks for image similarity to speech applications. To lower the friction of sharing these models, we introduce the model zoo framework:
First of all, we provide some trained models out of the box.
Each one of these can be downloaded by running
scripts/download_model_binary.py <dirname> where
<dirname> is specified below:
models/bvlc_reference_caffenet: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper.
models/bvlc_alexnet: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012.
models/bvlc_reference_rcnn_ilsvrc13: pure Caffe implementation of R-CNN.
User-provided models are posted to a public-editable wiki page.
A caffe model is distributed as a directory containing:
Github Gist is a good format for model info distribution because it can contain multiple files, is versionable, and has in-browser syntax highlighting and markdown rendering.
scripts/upload_model_to_gist.sh <dirname>: uploads non-binary files in the model directory as a Github Gist and prints the Gist ID. If
gist_idis already part of the
<dirname>/readme.mdfrontmatter, then updates existing Gist.
scripts/upload_model_to_gist.sh models/bvlc_alexnet to test the uploading (don’t forget to delete the uploaded gist afterward).
Downloading models is not yet supported as a script (there is no good commandline tool for this right now), so simply go to the Gist URL and click “Download Gist” for now.
It is up to the user where to host the
We host our BVLC-provided models on our own server.
Dropbox also works fine (tip: make sure that
?dl=1 is appended to the end of the URL).
scripts/download_model_binary.py <dirname>: downloads the
.caffemodelfrom the URL specified in the
<dirname>/readme.mdfrontmatter and confirms SHA1.