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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 (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/ 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 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 '<stream>/'


Ravi Teja Mullapudi (

Steven Chen (


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