Search Results for "matlab image classification"
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Showing 12 open source projects for "matlab image classification"
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation. ... -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
PaddleX is a deep learning full-process development tool based on the core framework, development kit, and tool components of Paddle. It has three characteristics opening up the whole process, integrating industrial practice, and being easy to use and integrate. Image classification and labeling is the most basic and simplest labeling task. Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the validation set and the test set. Therefore, we need to divide the above data. Using the paddlex command, the data set can be randomly divided into 70% training set, 20% validation set and 10% test set. ... -
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DomainBed
DomainBed is a suite to test domain generalization algorithms
...DomainBed also integrates multiple standard datasets—including RotatedMNIST, PACS, VLCS, Office-Home, DomainNet, and subsets from WILDS—allowing consistent experimentation across image classification tasks. -
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Uncertainty Baselines
High-quality implementations of standard and SOTA methods
...Rather than offering toy scripts, it provides end-to-end recipes—data input, model architectures, training loops, evaluation metrics, and logging—so results are comparable across runs and research groups. The library spans canonical modalities and tasks, from image classification and NLP to tabular problems, with baselines that cover both deterministic and probabilistic approaches. Techniques include deep ensembles, Monte Carlo dropout, temperature scaling, stochastic variational inference, heteroscedastic heads, and out-of-distribution detection workflows. Each baseline emphasizes reproducibility: fixed seeds, standard splits, and strong metrics such as calibration error, AUROC for OOD, and accuracy under shift. -
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ClassyVision
An end-to-end PyTorch framework for image and video classification
Classy Vision is a PyTorch-based framework designed for large-scale training and deployment of state-of-the-art image and video classification models. Developed by Facebook Research, it serves as an end-to-end system that simplifies the process of training at scale, reducing redundancy and friction in moving from research to production. Unlike traditional computer vision libraries that focus solely on modular components, Classy Vision provides a complete and unified framework, featuring distributed training, reproducible experiments, and flexible configuration tools. ... -
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whiteboxgui
An interactive GUI for WhiteboxTools in a Jupyter-based environment
...WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; etc. -
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cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that...
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Gluon CV Toolkit
Gluon CV Toolkit
...It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection, semantic segmentation and pose estimation, to instance segmentation and video action recognition. The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework. -
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RefineNet
RefineNet: Multi-Path Refinement Networks
RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. -
Dominate AI Search ResultsAthenaHQ is a cutting-edge platform for Generative Engine Optimization (GEO), designed to help brands optimize their visibility and performance across AI-driven search platforms like ChatGPT, Google AI, and more.
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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A cross-platform library that computes fast and accurate SIFT image features. libsiftfast provides Octave/Matlab scripts, a command line interface, and a python interface (siftfastpy). Optimized with SIMD instructions and OpenMP .
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golib
C++ collection mostly for image processing
libGo is a C++ class library containing all kinds of things that proved useful to me. Included are: - Linear algebra, using LAPACK and CBLAS - V4L(1) image grabber - Multithreading - Image containers (up to 3D) - Some simple optimisation code - Python embedding helper - Matlab interface - .. and other things, have a look at the HTML documentation! golib grew over many years, things I had use for have been added now and then. Some parts are better taken care of than others. ...
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