Code Monkey home page Code Monkey logo

deep-fbanks's Introduction

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.

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, and 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 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. 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

Code for CVPR14 paper (and Table 2 in arXiv paper): 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",
}

deep-fbanks's People

Contributors

kli-casia avatar mcimpoi avatar msubhransu avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.