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keras_frcnn Merge branch 'master' of Oct 16, 2017
.gitignore support for vgg16 network Jul 8, 2017
LICENSE Initial commit Jan 4, 2017 Update Mar 18, 2019 some code cleanup May 19, 2017
requirements.txt some code cleanup May 19, 2017 Fixed option type to int (#142) Sep 19, 2017 Update Mar 18, 2019


Keras implementation of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. cloned from

Please note that I currently am quite busy with other projects and unfortunately dont have a lot of time to spend on this maintaining this repository, but any contributions are welcome!


  • Both theano and tensorflow backends are supported. However compile times are very high in theano, and tensorflow is highly recommended.

  • can be used to train a model. To train on Pascal VOC data, simply do: python -p /path/to/pascalvoc/.

  • the Pascal VOC data set (images and annotations for bounding boxes around the classified objects) can be obtained from:

  • provides an alternative way to input data, using a text file. Simply provide a text file, with each line containing:


    For example:



    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 -o simple -p my_data.txt.

  • Running 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 for any testing.

  • can be used to perform inference, given pretrained weights and a config file. Specify a path to the folder containing images: python -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


  • 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:

ex1 ex2 ex3 ex4


  • If you get this error: ValueError: There is a negative shape in the graph!
    than update keras to the newest version

  • This repo was developed using python2. python3 should work thanks to the contribution of a number of users.

  • If you run out of memory, try reducing the number of ROIs that are processed simultaneously. Try passing a lower -n to Alternatively, try reducing the image size from the default value of 600 (this setting is found in

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