Caffe Model Zoo

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:

• A standard format for packaging Caffe model info.
• Tools to upload/download model info to/from Github Gists, and to download trained .caffemodel binaries.
• A central wiki page for sharing model info Gists.

Where to get trained models

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:

• BVLC Reference CaffeNet in models/bvlc_reference_caffenet: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in the NIPS 2012 paper.
• BVLC AlexNet in models/bvlc_alexnet: AlexNet trained on ILSVRC 2012, almost exactly as described in NIPS 2012.
• BVLC Reference R-CNN ILSVRC-2013 in models/bvlc_reference_rcnn_ilsvrc13: pure Caffe implementation of R-CNN.

User-provided models are posted to a public-editable wiki page.

Model info format

A caffe model is distributed as a directory containing:

• Solver/model prototxt(s)
• readme.md containing
  • YAML frontmatter
    • Caffe version used to train this model (tagged release or commit hash).
    • [optional] file URL and SHA1 of the trained .caffemodel.
    • [optional] github gist id.
  • Information about what data the model was trained on, modeling choices, etc.
  • License information.
• [optional] Other helpful scripts.

Hosting model info

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_id is already part of the <dirname>/readme.md frontmatter, then updates existing Gist.

Try doing 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.

Hosting trained models

It is up to the user where to host the .caffemodel file. 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 .caffemodel from the URL specified in the <dirname>/readme.md frontmatter and confirms SHA1.