Search Results for "matlab image classification" - Page 3
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Showing 74 open source projects for "matlab image classification"
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FixRes
Reproduces results of "Fixing the train-test resolution discrepancy"
FixRes is a lightweight yet powerful training methodology for convolutional neural networks (CNNs) that addresses the common train-test resolution discrepancy problem in image classification. Developed by Facebook Research, FixRes improves model generalization by adjusting training and evaluation procedures to better align input resolutions used during different phases. The approach is simple but highly effective, requiring no architectural modifications and working across diverse CNN backbones such as ResNet, ResNeXt, PNASNet, and EfficientNet. ... -
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PaddlePaddle models
Pre-trained and Reproduced Deep Learning Models
Pre-trained and Reproduced Deep Learning Models ("Flying Paddle" official model library, including a variety of academic frontier and industrial scene verification of deep learning models) Flying Paddle's industrial-level model library includes a large number of mainstream models that have been polished by industrial practice for a long time and models that have won championships in international competitions; it provides many scenarios for semantic understanding, image classification, target detection, image segmentation, text recognition, speech synthesis, etc. An end-to-end development kit that meets the needs of enterprises for low-cost development and rapid integration. The model library of Flying Paddle is an industrial-level model library tailored around the actual R&D process of domestic enterprises, serving enterprises in many fields such as energy, finance, industry, and agriculture. -
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Age and Gender Estimation
Keras implementation of a CNN network for age and gender estimation
...We pose the age regression problem as a deep classification problem followed by a softmax expected value refinement and show improvements over direct regression training of CNNs. Our proposed method, Deep EXpectation (DEX) of apparent age, first detects the face in the test image and then extracts the CNN predictions from an ensemble of 20 networks on the cropped face. -
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DeepCluster
Deep Clustering for Unsupervised Learning of Visual Features
DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups. This alternating “cluster & train” scheme helps the model... -
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StarGAN
Official PyTorch Implementation
...It demonstrates adversarial training strategies, domain classification losses, and generator-discriminator coordination required for stable multi-domain translation. Researchers and practitioners often use the project as a reference when studying conditional GANs and advanced image synthesis techniques. Overall, the repository provides a clean and practical baseline for experimenting with multi-domain generative modeling in PyTorch. -
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Spectral Python
A python module for hyperspectral image processing
Spectral Python (SPy) is a python package for reading, viewing, manipulating, and classifying hyperspectral image (HSI) data. SPy includes functions for clustering, dimensionality reduction, supervised classification, and more. -
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VGGFace2
VGGFace2 Dataset for Face Recognition
VGGFace2 is a large-scale face recognition dataset developed to support research on facial recognition across variations in pose, age, illumination, and identity. It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. It is split into a training set with 8,631 identities and a test set with 500 identities,... -
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QtiPlot is a user-friendly, platform independent data analysis and visualization application similar to the non-free Windows program Origin.
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ELI5
A library for debugging/inspecting machine learning classifiers
ELI5 is a Python library designed to help developers interpret, debug, and explain the predictions of machine learning models. The project focuses on improving model transparency by providing tools that visualize feature importance and prediction reasoning. It supports several popular machine learning frameworks including scikit-learn, XGBoost, LightGBM, CatBoost, and Keras. The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance... -
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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. -
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TensorImage
Image classification library for easily training and deploying models
...Moreover, TensorImage can also be used to classify on thousands of images with trained image classification models. -
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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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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. ... -
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Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). Using TensorFlow, it defines a meta-optimizer model that learns by observing and adapting to the optimization trajectories of other models. The project allows users to compare performance between traditional optimizers and the learned optimizer (L2L) on various benchmarks, demonstrating how optimization strategies can be learned through experience. ... -
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avimmir
(audio, video, image) Multimedia Multimodal Information Retrieval
audio classification; speaker segmentation; speaker clustering; speaker recognition; spoken document retrieval; image retrieval; video retrieval; etc. -
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PyVocabularyTree
A vocabulary tree for image classification using OpenCV
A vocabulary tree for image classification have been designed to be integrated in mobile robotic applications. It is a learning schema based on decission trees, bags of features and inverted files. The design provides training and optimization parameters that have been characterized using several detectors and descriptors for several input datasets. Evaluation tests performed on public image databases allow to compare obtained results with previously published literature. ... -
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Stanford Machine Learning Course
machine learning course programming exercise
The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy... -
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This is a c-library that provides tools for advanced analysis of electrophysiological data. It features denoising, unsupervised classification, time-frequency analysis, phase-space analysis, neural networks, time-warping and more.
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This is a Content Based Image Retrieval Interface with only color features implemented. This is part of a thesis work to analyze the different color features and observe the performance on mainly corel5k images.
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Open Source Particle Image Velocimetry
Open Source Particle Image Velocimetry and PIV Analysis
OpenPIV is an initiative of scientists to develop a software, algorithms and methods for the state-of-the-art experimental tool of Particle Image Velocimetry (PIV) which are free, open source, and easy to operate. -
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This toolkit allows complete control of a microscopy setup from Labview, Matlab, Scilab, Python, .Net, VB, IgorPro, Mathematica and more. Included is a standalone program for image acquisition and scripting control of a scientific microscope.
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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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nBoost is a suite of boosting algorithms designed to solve binary classification problems on data that is not linearly separable by a convex combination of base hypotheses, i.e. noisy data. WARNING: Active development. Underlying algorithm is unstable.
