Interspace-Beta-Backend Keeping it safe – AI Based COVID-19 Tracker using Deep Learning and facial recognition Usage Train the dataset and provide an unknown image in test directory python facerecog.py Predict the result and then start the script to scan that face. python already_trained.py This whole project was done in one night as a part of Hackathon so if there are any bugs, just report them. GitHub https://github.com/vanshwassan/Interspace-Beta    

Easy-ERA5-Trck Easy-ERA5-Trck is a super lightweight Lagrangian model for calculating thousands (even millions) of trajectories simultaneously and efficiently using ERA5 data sets. It can implement super simplified equations of 3-D motion to accelerate integration, and use python multiprocessing to parallelize the integration tasks. Due to its simplification and parallelization, Easy-ERA5-Trck performs great speed in tracing massive air parcels, which makes areawide tracing possible. Another version using WRF output to drive the model can be found here. Caution: Trajectory calculation is […]

Lobe This is a Home Assistant custom component for Lobe. Lobe is an AI tool that can classify images. This component lets you easily use an exported model along with another server to classify a camera entity’s feed with it. Installation Use HACS for the integration. You’ll also need a seperate server. Steps to install on another server: Install the Lobe library. Install Flask. Export a Tensorflow Lite model into a folder on the server. Copy over app.py and change […]

MoveNet Single Pose tracking on OpenVINO A convolutional neural network model that runs on RGB images and predicts human joint locations of a single person. Two variant: Lightning and Thunder, the latter being slower but more accurate. MoveNet uses an smart cropping based on detections from the previous frame when the input is a sequence of frames. This allows the model to devote its attention and resources to the main subject, resulting in much better prediction quality without sacrificing the […]

Abstract Large-scale unlabeled data has allowed recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for learning representations from images (MoCo and BYOL) use an inductive bias that different augmentations (e.g. random crops) of an image should produce similar embeddings. We show that such methods are vulnerable to backdoor attacks where an attacker poisons a part of the unlabeled data by adding a small trigger (known to the attacker) to the images. The model performance […]

federated federated is the source code for the Bachelor’s Thesis. Privacy-Preserving Federated Learning Applied to Decentralized Data (Spring 2021, NTNU) Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. In this project, the decentralized data is the MIT-BIH Arrhythmia Database. Features ML pipelines using centralized learning or federated learning. Support for the following aggregation methods: Federated Stochastic Gradient Descent […]

MLP-Mixer-CIFAR10 This repository implements MLP-Mixer as proposed in MLP-Mixer: An all-MLP Architecture for Vision. The paper introduces an all MLP (Multi-layer Perceptron) architecture for computer vision tasks. Yannic Kilcher walks through the architecture in this video. Experiments reported in this repository are on CIFAR-10. What’s included? Distributed training with mixed-precision. Visualization of the token-mixing MLP weights. A TensorBoard callback to keep track of the learned linear projections of the image patches. Notebooks Note: These notebooks are runnable on Colab. If […]

PyGAD: Genetic Algorithm in Python This project is part of PyGAD which is an open-source Python 3 library for building the genetic algorithm and optimizing machine learning algorithms (Keras & PyTorch). Check documentation of the GeneticAlgorithmPython project in the PyGAD’s documentation: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html PyGAD supports different types of crossover, mutation, and parent selection. PyGAD allows different types of problems to be optimized using the genetic algorithm by customizing the fitness function. The library is under active development and more features are […]

Clustering Predicting Baseball Metric Clusters: Clustering Application in Python Using scikit-learn This repository contains the prediction of baseball metric clusters using MLB Statcast Metrics. Goals Using MLB Statcast Metrics, summarize and examine baseball statistics. Build a k-Means Clustering model to predict clusters using exit velocity and launch angle as features. Determine the optimal number of clusters using the elbow method and silhouette coefficients. Build a Hierarchical (Agglomerative) Clustering model to predict clusters using exit velocity and launch angle as features. […]

OSCAR This repository contains the source code of our ICML 2021 paper How could Neural Networks understand Programs?. Environment Run following commands to build a docker image for the environment: cd docker sudo docker build -t oscar:latest . And you can launch a container with nvidia-docker command. sudo nvidia-docker run -it –mount type=bind,source=”$(pwd)”,target=/oscar oscar:latest To compile the binaries for processing the data: cd /oscar/bin make Then the OSCAR LLVM analyzer pass (located in analyzer), IR Lexer (located in irlexer), and […]