Visualization and diagnostics for cluster analysis in Python

Clustergram The clustergram was later implemented in R by Tal Galili, who also gives a thorough explanation of the concept. This is a Python translation of Tal’s script written for scikit-learn and RAPIDS cuML implementations of K-Means, Mini Batch K-Means and Gaussian Mixture Model (scikit-learn only) clustering, plus hierarchical/agglomerative clustering using SciPy. Alternatively, you can create clustergram using from_* constructors based on alternative clustering algorithms. Getting started You can install clustergram from conda or pip: conda install clustergram -c conda-forge […]

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Searching through photos from the air of lost people in the forest with python

lacmus The program for searching through photos from the air of lost people in the forest using Retina Net neural nwtwork. The project is being developed by a non-profit organization Liza Alert. Demonstration Picture 1 Picture 2 Training data You can download Lacmus Drone Dataset (LaDD) from mail.ru cloud You also can download Lacmus version of Stenford Drone Dataset (SDD) from mail.ru cloud GitHub https://github.com/lacmus-foundation/lacmus    

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A tool for monitoring and managing computing resources across multiple hosts

TensorHive TensorHive is an open source tool for monitoring and managing computing resources across multiple hosts. It solves the most common problems and nightmares about accessing and sharing your AI-oriented infrastructure across multiple, often competing users. It’s designed with simplicity, flexibility and configuration-friendliness in mind. Main features: GPU Reservation calendarEach column represents all reservation events for a GPU on a given day. In order to make a new reservation simply click and drag with your mouse, select GPU(s), add some […]

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Segmentation toolbox for EM connectomics

pytorch_connectomics The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individual synapses. Recent advances in electronic microscopy (EM) have enabled the collection of a large number of image stacks at nanometer resolution, but the annotation requires expertise and is super time-consuming. Here we provide a deep learning framework powered by PyTorch for automatic and semi-automatic semantic and instance segmentation in connectomics, which is called PyTorch Connectomics (PyTC). […]

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A Python implementation of the Ensemble Slice Sampling method

zeus zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Black-box inference, no hand-tuning, Excellent performance in terms of autocorrelation time and convergence rate, Scale to multiple CPUs without any extra effort. Example For instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like: import zeus import numpy as np def log_prob(x, ivar): return – 0.5 * np.sum(ivar * x**2.0) nsteps, […]

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Build a model which will take keywords as inputs and generate sentences as outputs

keytotext Idea is to build a model which will take keywords as inputs and generate sentences as outputs. Model: Keytotext is based on the Amazing T5 Model: k2t: Model k2t-tiny: Model k2t-base: Model mrm8488/t5-base-finetuned-common_gen (by Manuel Romero): Model Training Notebooks can be found in the Training Notebooks Folder Note: To add your own model to keytotext Please read Models Documentation Usage: Example usage: Example Notebooks can be found in the Notebooks Folder pip install keytotext UI: UI: pip install streamlit-tags […]

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PyTorch implementation of various fundamental RL algorithms

Reinforcement Learning (PyTorch) This repo will contain PyTorch implementation of various fundamental RL algorithms. It’s aimed at making it easy to start playing and learning about RL. The problem I came across investigating other DQN projects is that they either: Don’t have any evidence that they’ve actually achieved the published results Don’t have a “smart” replay buffer (i.e. they allocate (1M, 4, 84, 84) ~ 28 GBs! instead of (1M, 84, 84) ~ 7 GB) Lack of visualizations and debugging […]

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Machine Translation Weekly 79: More context in MT

The lack of broader context is one of the main problems in machine translation and in NLP in general. People tried various methods with actually quite mixed results. A recent preprint from Unbabel introduces an unusual quantification of context-awareness and based on that do some training improvements. The title of the paper is Measuring and Increasing Context Usage in Context-Aware Machine Translation and will be presented at ACL 2021. The paper measures how well informed the model is about the […]

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A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

CFPNet-M This repository contains the implementation of a novel light-weight real-time network (CFPNet-Medicine: CFPNet-M) to segment different types of biomedical images. It is a medical version of CFPNet, and the dataset we used from top to bottom are DRIVE, ISBI-2012, Infrared Breast, CVC-ClinicDB and ISIC 2018. The details of CFPNet-M and CFPNet can be found here respectively. Architecture of CFPNet-M CFP module CFPNet-M Dataset In this project, we test five datasets: [x] Infrared Breast Dataset [x] Endoscopy (CVC-ClinicDB) [x] Electron […]

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An all MLP solution to image classification in Pytorch

ResMLP – Pytorch Implementation of ResMLP, an all MLP solution to image classification out of Facebook AI, in Pytorch Install $ pip install res-mlp-pytorch Usage import torch from res_mlp_pytorch import ResMLP model = ResMLP( image_size = 256, patch_size = 16, dim = 512, depth = 12, num_classes = 1000 ) img = torch.randn(1, 3, 256, 256) pred = model(img) # (1, 1000) Citations @misc{touvron2021resmlp, title = {ResMLP: Feedforward networks for image classification with data-efficient training}, author = {Hugo Touvron and […]

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