Search Results for "process map"

The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I...

...This editor does not make you think. Just go to a mall, and start Every Door. You'll see mapped shops around you: tap on the checkmark for any that are still there, and add shops that are not on the map. That's the entire process: you can keep your entire town up-to-date thanks to this simple editor. There is also a micromapping mode for verifying and adding benches and street lamps. And an entrance mode for adding building attributes and entrances, which are merged automatically into building contours.

...Cognee acts a semantic memory layer, unveiling hidden connections within your data and infusing it with your company's language and principles. This self-optimizing process ensures ultra-relevant, personalized, and contextually aware LLM retrievals. Any kind of data works; unstructured text or raw media files, PDFs, tables, presentations, JSON files, and so many more. Add small or large files, or many files at once. We map out a knowledge graph from all the facts and relationships we extract from your data. ...

GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you load the text data, process the text data, and train models. To facilitate both the engineers and researchers, we provide command-line-toolkits for downloading and processing the NLP datasets. Gluon NLP makes it easy to evaluate and train word embeddings. Here are examples to evaluate the pre-trained embeddings included in the Gluon NLP toolkit as well as example scripts for training...

Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to...

...It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured at ICCV 2019), enabling researchers and practitioners to benchmark video classification models on large-scale datasets with over millions of labeled videos. The code demonstrates how to process frame-level features, train logistic and deep learning models, evaluate them using metrics like global Average Precision (gAP) and mean Average Precision (mAP), and export trained models for MediaPipe inference.

StoryText (formerly PyUseCase) is an unconventional GUI testing tool, with support for PyGTK, Tkinter, wxPython, Swing, SWT and Eclipse RCP. Instead of recording GUI mechanics directly, it asks the user for descriptive names and hence builds up a "domain language" along with a "UI map file" that translates it into the current GUI layout. The point is to reduce coupling, allow very expressive tests, and ensure that GUI changes mean changing the UI map file but not all the tests. Instead of an...

Encryption with symmetric-key algorithm Kirmah - generate keys with exotic chars - redefine key length - key mark to ensure decryption capabilities - mix data - fast with multiprocessing - possible compression - gui on gtk3 - cli tool - python3 DEPENDENCIES : ======== python3, python-gobject, gobject-introspection, pygtk 3 modes are available to encrypt : ======== - compression (full / disabled or only final step) - random (simulate a random order - based on crypted key - to randomize data) - mix (mix data according to a generated map - based on crypted key - with addition of noise) Process is as follow : ======== encrypt : file > [ compress > ] encrypt > [randomiz data > mix data > compress > ] file.kmh decrypt: file.kmh > [ uncompress > unmix data > unrandomiz data] > decrypt > [uncompress > ] file ======== for encrypt/decrypt large binary files, use the fastest alternative : split/merge

A web-based interface for the Hadoop MapReduce framework that simplifies the process of writing and running MapReduce jobs. Aimed at introducing parallelism concepts in introductory computer science courses.

Directory of geoinformational data and metadata, like georeferenced list of streets in your city, or list of known nomenclature map tiles, and tools to process this data.