ThunderSVM: A Fast SVM Library on GPUs and CPUs
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Updated
Apr 1, 2024 - C++
ThunderSVM: A Fast SVM Library on GPUs and CPUs
TensorRT-YOLO: A high-performance, easy-to-use YOLO deployment toolkit for NVIDIA, powered by TensorRT plugins and CUDA Graph, supporting C++ and Python.
This is a code repository for pytorch c++ (or libtorch) tutorial.
Caffe2 on iOS Real-time Demo. Test with Your Own Model and Photos.
Radar target classification, detection and recognition using deeplearning methods on MSTAR dataset
Direct Graphical Models (DGM) C++ library, a cross-platform Conditional Random Fields library, which is optimized for parallel computing and includes modules for feature extraction, classification and visualization.
在Android使用深度学习模型实现图像识别,本项目提供了多种使用方式,使用到的框架如下:Tensorflow Lite、Paddle Lite、MNN、TNN
A label tool aim to reduce semantic segmentation label time, rectangle and polygon annotation is supported
Darknet2ncnn converts the darknet model to the ncnn model
Remote Sensing and GIS Software Library; python module tools for processing spatial data.
A suite of tools for medical image processing focused on brain analysis
R package to Embed All the Things! using StarSpace
R wrapper for fastText
This is the implementation of Sparse Projection Oblique Randomer Forest
Pointnet++ modules implemented as tensorflow 2 keras layers.
FashionAI Clothes Attribute Recognition
Tools to detect and classify landmarks (currently, trees and pole-like objects) from point cloud data
Probabilistic question-asking system: the program asks, the users answer. The minimal goal of the program is to identify what the user needs (a target), even if the user is not aware of the existence of such a thing/product/service.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexi…
Extremely simple and fast extreme multi-class and multi-label classifiers.
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