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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:

./data/scripts/fetch_imagenet_models.sh

This script will populate data/imagenet_models.

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

./data/scripts/fetch_faster_rcnn_models.sh

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 https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/how-good-are-detection-proposals-really/. For COCO, using MCG is recommended over selective search. MCG boxes can be downloaded from http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/mcg/. Use the tool lib/datasets/tools/mcg_munge.py 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 https://dl.dropboxusercontent.com/s/o43o90bna78omob/instances_minival2014.json.zip?dl=0. Annotations for COCO val2014 (set) minus minival (~35k images) can be found at https://dl.dropboxusercontent.com/s/s3tw5zcg7395368/instances_valminusminival2014.json.zip?dl=0.

# 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