Search Results for "convolution" - Page 2
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Showing 85 open source projects for "convolution"
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DeepLabv3 Plus
Encoder-Decoder with Atrous Separable Convolution
DeepLabv3 Plus is a PyTorch implementation of DeepLabv3+ for semantic segmentation. It implements the encoder-decoder architecture with atrous separable convolution and provides a practical workflow for training, prediction, and mIoU evaluation. The repository supports VOC-style segmentation datasets and includes utilities for annotation generation, JSON dataset conversion, model summary inspection, prediction, and metric calculation. It provides pretrained weight workflows for MobileNetV2 and Xception backbones and notes that the correct backbone should be selected during training and prediction. ... -
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MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes. The framework introduces specialized layers such as edge-based convolution, mesh pooling, and mesh unpooling operations that enable hierarchical feature learning on mesh surfaces. ... -
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Vibe for Equalizer APO
Interface for Benchmarking Equalizer APO by using its Benchmark.exe
...Select your eq configuration and use Vibe to create an impulse response file. Then use this file in any app with convolution filtering including Equalizer APO. It filters (equalizes) the audio of this app. For instance, HQ Player 4 Desktop is such an app. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes obligatory when running a model. Mechanism like automatically breaking OpenCL kernel into small units is introduced to allow better preemption for the UI rendering task. ... -
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AlphaPlot
Interactive scientific graphing and data analysis software.
...The spreadsheets as well as graphs and note windows are gathered in a project and can be organized using folders. The built-in analysis operations include column/row statistics, (de)convolution, FFT and FFT-based filters. Scripting Console support in-place evaluation of mathematical expressions and scrtipting interface to ECMAScript like dynamic scripting language(java script). The GUI of the application uses the Qt toolkit. Periodic test builds are available here http://alphaplot.sourceforge.net/test-build.html -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to speed up inference and minimize memory footprint has been studied widely. ... -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. Each base class supports a different data format. -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
For quite some time now, we know about the benefits of transfer learning in Computer Vision (CV) applications. Nowadays, pre-trained Deep Convolution Neural Networks (DCNNs) are the first go-to pre-solutions to learn a new task. These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the models’ pre-trained weights, append a new classifier layer on top of it, and retrain the network. ... -
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HeSuVi
Headphone Surround Virtualizations for Equalizer APO
This tool imitates the 7.1 to binaural sound effect of many surround virtualizations by making use of Equalizer APO's convolution filter. Available are impulse response that were recorded with activated... – Dolby Atmos Headphone – CMSS-3D – SBX Pro Studio Surround (also found in BlasterX Acoustic Engine & THX TruStudio Pro) – Dolby Headphone – Sennheiser GSX Binaural 7.1 – DTS Headphone:X – Windows Sonic Headphone – Dolby Home Theater v4 Headphone Surround Virtualizer – Razer Surround – Out Of Your Head – Flux HEar V3 – OpenAL and DirectSound3D HRTFs – Waves Nx – and many more! ... -
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Takin
Inelastic neutron scattering suite.
The main development repository can be found here: https://code.ill.fr/scientific-software/takin -
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BasicSR
Winning Solution in NTIRE19 Challenges on Video Restoration
BasicSR is a deep learning framework designed for advanced video restoration tasks such as video super-resolution, deblurring, and denoising. Unlike single-image restoration models, EDVR addresses the temporal dimension by aligning multiple video frames using deformable convolutional layers in a coarse-to-fine manner, allowing it to effectively handle large motion and complex scene dynamics. The architecture includes bespoke modules (e.g., Pyramid, Cascading and Deformable alignment and... -
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PsPM
A matlab suite for Psycho-Physiological Modelling
PsPM is a powerful tool for psychophysiological modelling. It includes linear convolution models and non-linear models for SCR, and allows for modelling of other signals, too. A flexible import interface and many utilities make this software a standalone tool. IMPORTANT: As of 04.11.2019, the official code repository for PsPM has been moved to https://github.com/bachlab/PsPM . In order to check the latest releases, discussion and bug reports, please refer to our new repository. ... -
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DSP Lab
Digital Signal Processing Simulation
DSP Lab is a digital signal processing simulation application created to simulate and visualize process of sampling and filtering analog signal using DSP system. This application is created to provide as a tool for educator and student to visualize and understand DSP system. Source code is available at https://payhip.com/b/9mPY -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda (a... -
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ScorchCrafter Guitar FX DAW Plug-ins
Audio DSP FX and plug-ins, mostly for guitar (amp sim) and other FX
A group of audio DAW plug-ins targeting Windows (VST), Mac (VST/AU), and Linux, mostly for Guitar Amplifier simulation, with the C-120 being the flagship product (which started off long ago as a closed-source VST). Open source, mostly under GPL. -
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DRC generates digital room correction FIR filters to be used within HiFi systems in conjunction with real time convolution engines like BruteFIR.
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Batch PIE
A batch pipelined image editor
Current filter functionality: - Simple editing options: Image cropping, resizing, rotation, Color brightness curve alignment - Histobram processing: Convolution, statistics (e. g. f_max or median analysis) - Image segmentation: The actual segmentation process as well as group weight calculation for further filtering (both functions rely on self defined custom dynamic mathematical functions) - Dynamic mathematical functions for custom and automated image filtering: General mathematical operations, using image or matrix as f(x, y), export f(x, y) as image or matrix, mapping variables on other ones and of course boolean operation for case sensitivity - A flexible variables model of dynamic mathematical function that sets no restriction on particular variables count - Sub project support for an organized total process targeting to save time using previously created editing routines instead of redoing steps each time -
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HyperFun is a geometric modeling language and software tools supporting implicit surfaces, convolution surfaces, constructive solids, and more general function representation (FRep) objects.
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DaNNet
Deep Artificial Neural Network framework using Armadillo
DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network. -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
...Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution filters to extract n-gram features that are then pooled and fed into a classifier. The project includes scripts for training, evaluation, and data handling, making it easy to run experiments on datasets such as movie reviews or other labeled text collections. By breaking down the model into understandable components, it serves as a practical reference for students and practitioners learning how deep learning models handle text beyond traditional bag-of-words approaches. -
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Toolbox
Piotr's Image & Video Matlab Toolbox
Piotr’s Image & Video MATLAB Toolbox is a general-purpose MATLAB toolbox for image and video processing and vision tasks, offering utilities, filters, detection, feature extraction, and algorithm building blocks. Example and demo scripts for usage (e.g. acfReadme, detector readmes). It augments MATLAB’s native capabilities (not replacing the Image Processing Toolbox) by providing efficient, reusable wrappers and optimized routines. Example and demo scripts for usage (e.g. acfReadme, detector... -
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DC-TTS
TensorFlow Implementation of DC-TTS: yet another text-to-speech model
DC-TTS is a TensorFlow implementation of the DC-TTS architecture, a fully convolutional text-to-speech system designed to be efficiently trainable while producing natural speech. It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN... -
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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. It includes data preprocessing helpers, training scripts, and configuration options so developers can experiment with different filter sizes, dropout rates, and hyperparameters to optimize performance for their dataset. -
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BlockSparse
Efficient GPU kernels for block-sparse matrix multiplication
...The repo implements both blocksparse and blockwise convolution/transpose-convolution primitives, with support for preparing, executing, and verifying those ops on NVIDIA GPUs. In addition to low-level kernels, it includes wrapper code for integrating with TensorFlow, example scripts (e.g. a transformer on the enwik8 dataset), transformer logic that uses blocksparse operations, and debugging helpers.
