Florent Forest's starred repositories
RetroPie-Setup
Shell script to set up a Raspberry Pi/Odroid/PC with RetroArch emulator and various cores
amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
pyannote-audio
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
jiffyreader.com
A Browser Extension for faster reading on ANY website!
pyclustering
pyclustering is a Python, C++ data mining library.
cross-domain-detection
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation [Inoue+, CVPR2018].
direkt.bahn.guru
All direct long-distance railway connections for cities in and beyond central Europe.
awesome-domain-adaptation-object-detection
A collection of papers about domain adaptation object detection. Welcome to PR the works (papers, repositories) that are missed by the repo.
spark-iforest
Isolation Forest on Spark
amazon-sagemaker-build-train-deploy
Scale complete ML development with Amazon SageMaker Studio
scikit-decide
AI framework for Reinforcement Learning, Automated Planning and Scheduling
cityscapes-to-coco-conversion
Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron
crackseg9k
[ECCV W 2022] "CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks" by Shreyas Kulkarni, Shreyas Singh, Dhananjay Balakrishnan, Siddharth Sharma, Saipraneeth Devunuri, Sai Chowdeswara Rao Korlapati.
aws-lambda-python-opencv-layer
Provides a pre-built Python OpenCV layer for AWS Lambda.
TensorClus
TensorClus, Tensor co-clustering, text mining, clustering, multiple graphs
Pavement-Distress-Classification
This repo collects some datasets and papers about Pavement Distress Classification. Moreover, all code will be integrated into this repo.
ENACdrives
ENAC facility to access all user's NAS
MultiCoclustering
Scala implementation of the Multiple Coclustering model, a Bayesian Non-Parametric method for block clustering of multivariate continuous observations
SMDA
The present repository implement a method that tackles the issues of model interpretability and variable importance in random forests, in the presence of correlated input variables. Variable importance criteria based on random permutations are known to be sensitive when input variables are correlated, and may lead for instance to unreliability in the importance ranking. In order to overcome some of the problems raised by correlation, an original variable importance measure is introduced. The proposed measure builds upon an algorithm which clusters the input variables based on their correlations, and summarises each such cluster by a synthetic variable.