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minimal-bag-of-visual-words-image-classifier

Implementation of a content based image classifier using the bag of visual words approach in Python together with Lowe's SIFT and Libsvm.

mirrormotion

Connector between Kinect gesture recognition and user-defined actions

mkct-tracker

Code of MKCT-Tracker v1.0 (Matlab Version for Discussion)

ml-contest-face-verification

ml-hv-grid-pub

Code for high-voltage grid mapping project with the World Bank; early 2018

ml-medimage

The ML-MedImage framework provides an environment to evaluating multi-label learners to the automatic annotation task of two-dimensional medical images. The label are assigned conform to the IRMA code. In this framework, ten subsets are built from a set with more than 12.000 ray-X medical images from chest region. The EHD, Gabor, LBP and SIFT techniques are used to feature the samples from formed subsets. From theses subsets, the performances of various multi-label learners are evaluated on image annotation task. Ten iterations are performed to this evaluating. For each iteration, a subset is used to train step and nine remaining subsets to test step. The learners used are BRkNN, ClassifierChain(RandomForest), LabelPowerset(kNN) and MLkNN. Beyond from this approach, an alternative approach is evaluating too. In this other approach, the classification is performed to axis from IRMA code instead of to assign the labels to all axes in one step like to first approach. The evaluating provides results to various measures for each iteration. These results are grouped by measure in individual files to that can get means and standard deviation from each iteration. The measures considered in experimental evaluating performed are Average Precision, Hamming Loss and Micro F.

ml4a-ofx

A collection of openFrameworks apps for working with machine learning

mlkp

CVPR18 Paper: Multi-scale Location-aware Kernel Representation for Object Detection

mlmi_ex7_cnn

Course: Machine Learning in Medical Imaging 2016 - Exercise 07 - Convolutional Neural Network (MatConvNet)

mlnd-capstone

Lane Detection with Deep Learning - My Capstone project for Udacity's ML Nanodegree

mlsd

Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection" (AAAI 2022)

mltools

A collection of Machine Learning Tools for object detection and classification on DG imagery.

mlx

Machine Learning Explorations - A list of machine learning resources

mmdetection

Open MMLab Detection Toolbox and Benchmark

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

mnc

Instance-aware Semantic Segmentation via Multi-task Network Cascades

mneuron

mnist-fun

Analysis of the Effect of Noisy Data on CNN Performance with MNIST

mobile-id

Deep Face Model Compression

mobilenet-caffe

Caffe Implementation of Google's MobileNets (v1 and v2)

mobilenet-ssd

Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

mobilenet-ssd-1

Ultra-fast MobileNet-SSD + Neural Compute Stick(NCS) than YoloV2 + Explosion speed by RaspberryPi · Multiple moving object detection with high accuracy.　YoloV2 より超速 MobileNetSSD+Neural Compute Stick(NCS)+Raspberry Piによる爆速・高精度の複数動体検知

mobilenet_object_detection

Rapid object detection suitable for implementation in autonomous vehicles.

mobilenetv3.pytorch

73.2% MobileNetV3-Large and 67.1% MobileNetV3-Small model on ImageNet

model-compression

This is my final year project of Bachelor of Engineering. Its still incomplete though. I am trying to replicate the research paper "Deep Compression" by Song Han et. al. This paper received best paper award in ICLR 2016

models

Models and examples built with TensorFlow

modern-cpp-features

A cheatsheet of modern C++ language and library features.

mofreak

Action recognition for surveillance scenarios with local binary feature descriptors

mongoengine

A Python Object-Document-Mapper for working with MongoDB

mongoguestbook

Small guestbook application written in Python using Bottle and PyMongo