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3d_vision_ideaversal

cpu-gpu-monitor-python

cpu-gpu-monitor-python

cvx_learning

convex optimization learning notes

fathom_pre

Deep neural networks are developping fast. During these years, a lot of ideas have been put forward. Such as AlexNet, NIN(network in network), ResNet(Residual Network), Inception net, RNN(Recurrent Neural Network), LSTM(long short term memory network), etc. And all these have been used in all domains of human life. My work was to study the usage of these neural network within the embedded system Fathom USB. The labouratory U2IS of ENSTA-Paristech, as a partner of Movidius company, has an evalution prototype of Fathom USB stick, which is an embedded calculation system based on Manycore Myriad-2 processor. It is equipped with a pseudo-compilation for neural network, which can transform the deep network of the framework Caffe or TensorFlow into an optimized binary code that can be processed by Myriad-2 [13]. First object was to test the stick for the recognition of images, type Cifar-10 [11]. In the second part, the object was to design a network compatible with Fathom in practical problem, the recognition of plankton in projet H2020 BRIDGES EcoTaxa [10], which offers a rich data base of non-uniform taxonomic tree.

foobar

foobar

for_fun

Nature Philosophy reading notes

gggliuye.github.io

gnn_learning

graph neural network leaning notes

graph_based_image_segmentation

graph based image segmentation (graph-cut, TV-L1, etc)

handwriting_number

kaggles

pfe_mid_term

Exploration des algorithmes de deep-learning pour la correspondance entre la vision et un espace 3D

pointnetkitti

PointNet based point cloud semantic segmentation.

stock_predict

superpanoama

superpoint-superglue-panoama image localization

vins

vins bazel

vins_bastian

Vins designed for raspberry pi

vio

SLAM related learning notes

webar

js based opencv marker image ar