Please refer to the homepage (http://www.vlfeat.org/matconvnet) for releases, data, and documentation.
MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient (integrating MATLAB GPU support), and can run and learn state-of-the-art CNNs, similar to the ones achieving top scores in the ImageNet challenge. Several example CNNs are included to classify and encode images.
An important feature of MatConvNet is making available the CNN building blocks as easy-to-use MATLAB commands. This allows prototyping new CNN architectures and learning algorithms as well as recycling fast convolution code for sliding window object detection and other applications.
MatConvNet is developed by a team of computer vision scientists in Oxford and other research institutions.
Cite As
Andrea Vedaldi (2024). vlfeat/matconvnet (https://github.com/vlfeat/matconvnet), GitHub. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
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- AI and Statistics > Deep Learning Toolbox >
- Image Processing and Computer Vision > Computer Vision Toolbox > Recognition, Object Detection, and Semantic Segmentation >
- Parallel Computing > Parallel Computing Toolbox > GPU Computing >
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Acknowledgements
Inspired: electroCUDA
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examples
examples/+solver
examples/cifar
examples/custom_imdb
examples/fast_rcnn
examples/fast_rcnn/+dagnn
examples/fast_rcnn/bbox_functions
examples/fast_rcnn/datasets
examples/imagenet
examples/mnist
examples/spatial_transformer
examples/vggfaces
matlab
matlab/+dagnn
matlab/+dagnn/@DagNN
matlab/compatibility/parallel
matlab/simplenn
matlab/xtest
matlab/xtest/suite
utils
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1.1.0.0 | Updates the description. |
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1.0.0.0 |
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