-
samgis-be
SamGIS can do machine learning-based (Segment Anything by Meta - Facebook) image segmentation tasks applied to GIS and geo data (there is also a SPA frontend similar to https://github.com/trincadev/samgis-fe used on HuggingFace)
-
Scout Monitoring
Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
-
powertools-lambda-python
A developer toolkit to implement Serverless best practices and increase developer velocity.
-
-
-
MobileSAM
This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
This project, composed by a SPA frontend and by a backend (samgis-be), is an attempt to perform machine learning image segmentation on geo-spatial data even without the use of dedicated graphics cards. I recently added the ability to start from natural language text prompts.
I wrote the backend adapting SAM Exporter with the aim to use the machine learning segment anything project for the purpose to improve polygons recognition in GIS web applications.
Starting from version 1.5.1 the backend integrates changes borrowed from sam_onnx_full_export, to support OnnxRuntime 1.17.x and later versions. Please note that on MacOS directly running the project from the command line suffers from memory leaks, making inference operations slower than normal. It's best therefore running the project inside a docker container, unless in case of development or debugging activities.
Setup not always easy (I used Powertools for AWS Lambda (Python) to improve my logger setup)
Starting from version 1.5.1 the backend integrates changes borrowed from sam_onnx_full_export, to support OnnxRuntime 1.17.x and later versions. Please note that on MacOS directly running the project from the command line suffers from memory leaks, making inference operations slower than normal. It's best therefore running the project inside a docker container, unless in case of development or debugging activities.
To avoid abuse the prompt input web map that I host on my personal site is under authentication (I use auth0.com).
it instantiates a MobileSam model instance if not already created using OnnxRuntime as runtime