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model
LICENSE.md
README.md
coco.lua
dataset.lua
example_loader.lua
fbnn_Optim.lua
opts.lua
parallel_batch_loader.lua
preprocess.lua
test.lua
util.lua

README.md

DOCK

Code for the [DOCK: Detecting Objects by transferring Common-sense Knowledge, ECCV 2018] Krishna Kumar Singh, Santosh Divvala, Ali Farhadi, Yong Jae Lee (https://dock-project.github.io/)

If you use our work, please cite it:

@inproceedings{singh-eccv2018,
  title = {DOCK: Detecting Objects by transferring Common-sense Knowledge},
  author = {Krishna Kumar Singh, Santosh Divvala, Ali Farhadi, and Yong Jae Lee},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2018}
}

This code is based on the conntextlonet code of Vadim Kantorov (https://github.com/vadimkantorov/contextlocnet)

Pre-requisites

Please follow the steps of https://github.com/vadimkantorov/contextlocnet

Pre-trained Models

You can find the pre-trained models here: https://drive.google.com/open?id=1QobgtcKX06QOY70Og3ezoRm_1Gns5112

Test Model

th test.lua <pre-trained model>  <unique_id>

The above command will apply pre-trained model on all the MS COCO test images and gives the class scores for each proposal. You can use unique_id to specify the name of file which would be used to save the test scores.

More code coming soon