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This directory holds (after you download them):

  • Caffe models pre-trained on ImageNet
  • Faster R-CNN models
  • Symlinks to datasets

To download Caffe models (ZF, VGG16) pre-trained on ImageNet, run:


This script will populate data/imagenet_models.

To download Faster R-CNN models trained on VOC 2007, run:


This script will populate data/faster_rcnn_models.

In order to train and test with PASCAL VOC, you will need to establish symlinks. From the data directory (cd data):

# For VOC 2007
ln -s /your/path/to/VOC2007/VOCdevkit VOCdevkit2007

# For VOC 2012
ln -s /your/path/to/VOC2012/VOCdevkit VOCdevkit2012

Install the MS COCO dataset at /path/to/coco

ln -s /path/to/coco coco

For COCO with Fast R-CNN, place object proposals under coco_proposals (inside the data directory). You can obtain proposals on COCO from Jan Hosang at For COCO, using MCG is recommended over selective search. MCG boxes can be downloaded from Use the tool lib/datasets/tools/ to convert the downloaded MCG data into the same file layout as those from Jan Hosang.

Since you'll likely be experimenting with multiple installs of Fast/er R-CNN in parallel, you'll probably want to keep all of this data in a shared place and use symlinks. On my system I create the following symlinks inside data:

Annotations for the 5k image 'minival' subset of COCO val2014 that I like to use can be found at Annotations for COCO val2014 (set) minus minival (~35k images) can be found at

# data/cache holds various outputs created by the datasets package
ln -s /data/fast_rcnn_shared/cache

# move the imagenet_models to shared location and symlink to them
ln -s /data/fast_rcnn_shared/imagenet_models

# move the selective search data to a shared location and symlink to them
# (only applicable to Fast R-CNN training)
ln -s /data/fast_rcnn_shared/selective_search_data

ln -s /data/VOC2007/VOCdevkit VOCdevkit2007
ln -s /data/VOC2012/VOCdevkit VOCdevkit2012
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