C3d Keras

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C3D Model for Keras + TensorFlow

The scripts here are inspired by C3D Model for Keras gist, but specifically for Keras + TensorFlow (not Theano-backend).

To reproduce results:

  • Run a script that does everything: bash do_everything.sh

OR, build a docker image, which will do all the steps of replication during the build:

docker build -t c3d-keras .

OR, run each of these steps:

  1. Download pretrained model: bash models/get_weights_and_mean.sh
  2. Download sport1mil labels: bash sports1m/get_labels.sh
  3. Download facebook/C3D caffe.proto file for conversion from caffe to Keras: wget https://raw.githubusercontent.com/facebook/C3D/master/C3D-v1.0/src/caffe/proto/caffe.proto
  4. Install protobuf per instruction in google/protobuf. In Ubuntu, sudo apt-get install protobuf-compiler will do.
  5. Compile the caffe.proto file for python: protoc --python_out=. caffe.proto
  6. Make sure the default keras config (in ~/.keras/keras.json) has: tf image_dim_ordering, and tensorflow backend.
  7. Convert the pre-trained model from Caffe format to Keras: python convert_caffe_model.py
  8. Download test video: bash download_test_video.sh
  9. Run test: python test_model.py


Known to work with the following python packages:

  • Keras==2.0.0
  • tensorflow==0.12.1
  • h5py==2.6.0
  • numpy==1.12.0
  • cv2==3.1.0
  • pydot==1.1.0
  • graphviz

Some basic command-line tools:


A following classification probability plot is expected (saved as probabilities.png). A peak at 367th class (probability = 71%) corresponds to basketball label.

Classification Probability Plot

The top 5 labels will also be reported, and should look something like:

Position of maximum probability: 367
Maximum probability: 0.57953
Corresponding label: basketball

Top 5 probabilities and labels:
basketball: 0.57953
volleyball: 0.14435
streetball: 0.06718
freestyle wrestling: 0.03323
greco-roman wrestling: 0.03293


  1. C3D Model for Keras
  2. Original C3D implementation in Caffe
  3. C3D paper

Comment / Feedback

Feel free to contact Chuck Cho (cycho at axon.com) for any comment or feedback.


  • Source code: 2-clause BSD.
  • Data: various Creative Commons licenses. See LICENSE.md for details.
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