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Online Model Distillation for Efficient Video Inference

This public repository is currently a work in progress. Features currently supported include the JITNet architecture, pretraining on the COCO dataset, and model timing.

Getting Started

Please install the following (Our versions shown in parentheses):

  • Python 3, recommended through Anaconda (Python 3.6.5, Anaconda 4.5.11)
  • CUDA and cuDNN (CUDA 9.2, cuDNN 7.3, NVIDIA-396 driver)
  • Tensorflow (Tensorflow 1.10.1. We recommend to build from source for higher performance)

Clone this repository, then initialize submodules with git submodule update --init --recursive.

Pretraining on the COCO Dataset

To pretrain JITNet on the COCO Dataset, first download and set up the COCO-stuff dataset from https://github.com/nightrome/cocostuff (TODO: detailed instructions)

TODO: detailed instructions on pretraining using the script.

Timing the JITNet model

We include a timing script to determine JITNet inference time with different architecture setups and hardware/software configurations. Run it with python utils/time_models.py. The script times the default JITNet architecture setup: this can be changed by changing the arguments at the end of the script.

The Long Video Streams (LVS) Dataset

The Long Video Streams Dataset can be found at https://olimar.stanford.edu/hdd/lvsdataset/. Each folder contains one stream, consisting of multiple video chunks and corresponding Mask R-CNN predictions. You can download an individual stream, or the whole dataset, using wget. For instance, this will download a stream into lvsdataset/ (use the same command without the stream to download the whole dataset):

wget -e robots=off -r -nH --cut-dirs 1 --no-check-certificate -np 'https://olimar.stanford.edu/hdd/lvsdataset/<stream>/'

Contact

Ravi Teja Mullapudi (rmullapu@cs.cmu.edu)

Steven Chen (stevenzc@stanford.edu)

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