keras-frcnn
Keras implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.
USAGE:
Both theano and tensorflow backends are supported. However compile times are very high in theano, and tensorflow is highly recommended.
train_frcnn.py
can be used to train a model. To train on Pascal VOC data, simply do: python train_frcnn.py -p /path/to/pascalvoc/
.
the Pascal VOC data set (images and annotations for bounding boxes around the classified objects) can be obtained from: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
simple_parser.py provides an alternative way to input data, using a text file. Simply provide a text file, with each line containing:
filepath,x1,y1,x2,y2,class_name
For example:
/data/imgs/img_001.jpg,837,346,981,456,cow
/data/imgs/img_002.jpg,215,312,279,391,cat
The classes will be inferred from the file. To use the simple parser instead of the default pascal voc style parser, use the command line option -o simple
. For example python train_frcnn.py -o simple -p my_data.txt
.
Running train_frcnn.py
will write weights to disk to an hdf5 file, as well as all the setting of the training run to a pickle
file. These settings can then be loaded by test_frcnn.py
for any testing.
test_frcnn.py can be used to perform inference, given pretrained weights and a config file. Specify a path to the folder containing images: python test_frcnn.py -p /path/to/test_data/
Data augmentation can be applied by specifying --hf
for horizontal flips, --vf
for vertical flips and --rot
for 90 degree rotations
NOTES:
config.py contains all settings for the train or test run. The default settings match those in the original Faster-RCNN paper. The anchor box sizes are [128, 256, 512] and the ratios are [1:1, 1:2, 2:1].
The theano backend by default uses a 7x7 pooling region, instead of 14x14 as in the frcnn paper. This cuts down compiling time slightly.
The tensorflow backend performs a resize on the pooling region, instead of max pooling. This is much more efficient and has little impact on results.
Example output:
ISSUES:
If you get this error: ValueError: There is a negative shape in the graph!
than update keras to the newest version
Make sure to use python2
, not python3
. If you get this error: TypeError: unorderable types: dict() < dict()
you are using python3
If you run out of memory, try reducing the number of ROIs that are processed simultaneously. Try passing a lower -n
to train_frcnn.py
. Alternatively, try reducing the image size from the default value of 600 (this setting is found in config.py
.