Pytorch implementation of count-ception on the MBM dataset. Please refer to the [original repository] (https://github.com/ieee8023/countception) (with Theano and Lasagna deep learning frameworks) for more details on other datasets.

• Pytorch
• Scikit-Image

I included a Pickle file of the dataset similar to how it was prepared in the original repository's MBM code. To re-generate the pickle file, you would need to run 'create_datafiles.py' making sure to specify the dataset directory.

To train a model, run the following command:

python train.py --pkl-file 'utils/MBM-dataset.pkl' --batch-size 2 --epochs 1000 --lr 0.001

To test the model, run the following command:

python test.py --pkl-file 'utils/MBM-dataset.pkl' --batch-size 1 --ckpt 'checkpoints/after_950_epochs.model'

Count-ception: Counting by Fully Convolutional Redundant Counting
JP Cohen, G Boucher, CA Glastonbury, HZ Lo, Y Bengio
International Conference on Computer Vision (ICCV) Workshop on Bioimage Computing

@inproceedings{Cohen2017,
title = {Count-ception: Counting by Fully Convolutional Redundant Counting},
author = {Cohen, Joseph Paul and Boucher, Genevieve and Glastonbury, Craig A. and Lo, Henry Z. and Bengio, Yoshua},
booktitle = {International Conference on Computer Vision Workshop on BioImage Computing},
url = {http://arxiv.org/abs/1703.08710},
year = {2017}
}