# Deep learning approach to bacterial colony classification

This is fork from [https://github.com/mcimpoi/deep-fbanks](https://github.com/mcimpoi/deep-fbanks). The code was appropriatelly modified in order to cover DIBaS dataset and new classifiers.

In order to run the experiments presented by Zieliński et al. in "Deep learning approach to bacterial colony classification", the following has to be done:

* Download the following code
* Install all external libraries and download CNN models, as described below (DO NOT download texture databases like FMD - there are not needed)
* Download DIBaS dataset from [http://misztal.edu.pl/software/databases/dibas/](http://misztal.edu.pl/software/databases/dibas/) and move it into data folder, so there is file with the following path: ./deep-fbanks/data/bacteria/Acinetobacter.baumanii/Acinetobacter.baumanii_0001.tif etc.
* Run run_experiment.m (NEEDED Matlab 2017a or newer)


# Deep filter banks for texture recognition, description, and segmentation

The provided code evaluates R-CNN and FV-CNN descriptors on various texture and material datasets (DTD, FMD, KTH-TIPS2b, ALOT), as well as for other datasets: objects (PASCAL VOC 2007), scenes (MIT Indoor), and fine-grained (CUB 200-2011). The results of these experiments are described in Table 1 and 2 of ** Cimpoi15 ** and Tables 3, 4, 5, and 6 of ** Cimpoi15a. **
 

##   Getting starded

After downloading the code, make sure that the dependencies are resolved (see below).

You also have to download the datasets you want to evaluate on, and link to them or copy them under data folder, in the location of your repository. Download the models (VGG-M, VGG-VD and AlexNet) in `data/models`. It is slightly faster to download them manually from here: [http://www.vlfeat.org/matconvnet/pretrained](http://www.vlfeat.org/matconvnet/pretrained).

Once done, run the `run_experiments.m` file.

In `run_experiments.m` you could remove (or add) dataset names to the `datasetList` cell. Make sure you adjust the number of splits accordingly. The datasets are specified as `{'dataset_name', <num_splits>}` cells.

### Dependencies

The code relies on [vlfeat](http://www.vlfeat.org/), and [matconvnet](http://www.vlfeat.org/matconvnet), which should be downloaded and built before running the experiments.
Run git submodule update -i in the repository download folder.

To build `vlfeat`, go to `<deep-fbanks-dir>/vlfeat` and run make; ensure you have MATLAB executable and mex in the path.

To build `matconvnet`, go to `<deep-fbanks-dir>/matconvnet/matlab` and run `vl_compilenn`; ensure you have CUDA installed, and nvcc in the path.

For LLC features (Table 3 in arxiv paper), please download the code from [http://www.robots.ox.ac.uk/~vgg/software/enceval_toolkit](http://www.robots.ox.ac.uk/~vgg/software/enceval_toolkit) and copy the following to the code folder (no subfolders!)

* `enceval/enceval-toolkit/+featpipem/+lib/LLCEncode.m`
* `enceval/enceval-toolkit/+featpipem/+lib/LLCEncodeHelper.cpp`
* `enceval/enceval-toolkit/+featpipem/+lib/annkmeans.m`

Create the corresponding dcnnllc encoder type (see the examples provided in `run_experiments.m` for BOVW, VLAD or FV).

### Paths and datasets

The `<dataset-name>_get_database.m` files generate the `imdb` structure for each dataset. Make sure the datasets are copied or linked to manually in the data folder for this to work.

The datasets are stored in individual folders under data, in the current code folder, and experiment results are stored in `data/exp01` folder, in the same location as the code. Alternatively, you could make data and experiments symbolic links pointing to convenient locations.

Please be aware that the descriptors are stored on disk (in cache folder, under `data/exp01/<experiment-dir>`), and may require large amounts of free space (especially FV-CNN features).


### Dataset and evaluation

Describable Textures Dataset (DTD) is publicly available for download at:
[http://www.robots.ox.ac.uk/~vgg/data/dtd](http://www.robots.ox.ac.uk/~vgg/data/dtd). You can also download the precomputed DeCAF features for DTD, the paper and evaluation results.

Our additional annotations for OpenSurfaces dataset are publicly available for download at:
[http://www.robots.ox.ac.uk/~vgg/data/wildtex](http://www.robots.ox.ac.uk/~vgg/data/wildtex)

Code for CVPR14 paper (and Table 2 in arXiv paper):
[http://www.robots.ox.ac.uk/~vgg/data/dtd/download/desctex.tar.gz](http://www.robots.ox.ac.uk/~vgg/data/dtd/download/desctex.tar.gz)

###   Citation

If you use the code and data please cite the following in your work:

FV-CNN code and additional annotaitons for the OpenSurfaces dataset:

	@article{Cimpoi15a,
  	Author       = "Cimpoi, M. and Maji, S., Kokkinos, I. and Vedaldi, A.",
  	Title        = "Deep Filter Banks for Texture Recognition, Description, and Segmentation"
  	Journal      = "arXiv preprint arXiv:1507.02620",
  	Year         = "2015",
	}

	@inproceedings{Cimpoi15,
  	Author       = "Cimpoi, M. and Maji, S. and Vedaldi, A.",
  	Title        = "Deep Filter Banks for Texture Recognition and Segmentation",
  	Booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition",
  	Year         = "2015",
	}

DTD dataset and IFV + DeCAF baseline:

	@inproceedings{cimpoi14describing,
  	Author       = "M. Cimpoi and S. Maji and I. Kokkinos and S. Mohamed and A. Vedaldi",
  	Title        = "Describing Textures in the Wild",
  	Booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition",
  	Year         = "2014",
	}