This repo contains a Keras implementation of the paper, Class-Agnostic Counting (Lu et al., ACCV 2018). It includes code for training the GMN (Generic Matching Network) and adapting it to specific datasets.

• Python 3.4.9
• Keras 2.1.5
• Tensorflow 1.6.0

Download and preprocess the data for training the GMN following the instructions at: https://github.com/bertinetto/siamese-fc/tree/master/ILSVRC15-curation [1]. Before preprocessing the dataset, change the following variables:

    exemplar_size = 63;
    instance_size = 255;
    context_amount = 0.1;

The following datasets were used for the adaptation experiments:

• VGG synthetic cells [2]
• HeLa cells [3]
• CARPK cars [4]

Labels should be in the form of dot annotation images.

To train the Generic Matching Network (GMN) on the ImageNet video data, run

python src/main.py --mode pretrain --data_path /path/to/ILSVRC2015_crops/train/

The code expects ImageNet pretrained Resnet50 weights at

models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

To adapt a trained GMN to a specific dataset, e.g. vgg cells, run

python src/main.py --mode adapt --dataset vgg_cell --data_path /path/to/data --gmn_path /path/to/pretrained_gmn_model

The pretrained GMN weights are available here.

[1] L. Bertinetto, J. Valmadre, J.F. Henriques, A. Vedaldi, P.H.S. Torr. "Fully-Convolutional Siamese Networks for Object Tracking." In ECCV Workshop 2016.
[2] V. Lempitsky and A. Zisserman. "Learning to Count Objects in Images." In NIPS 2010.
[3] C. Arteta, V. Lempitsky, J. A. Noble, A. Zisserman. "Learning to Detect Cells Using Non-overlapping Extremal Regions." In MICCAI 2012.
[4] M. Hsieh, Y. Lin, W. Hsu. "Drone-based Object Counting by Spatially Regularized Regional Proposal Networks." In ICCV 2017.
[5] W. Xie, J. A. Noble, A. Zisserman. "Microscopy Cell Counting with Fully Convolutional Regression Networks." In MICCAI Workshop 2016.

@InProceedings{Lu18,
  author       = "Lu, E. and Xie, W. and Zisserman, A.",
  title        = "Class-agnostic Counting",
  booktitle    = "Asian Conference on Computer Vision",
  year         = "2018",
}