sohiniroych

U-net-for-Multi-class-semantic-segmentation

ML-Monitoring-with-Grafana

Unet-using-TF2

This example demonstrates the use of U-net model for retinal blood vessel segmentation on tensorflow 2.x

Volvo-DataX

AI_with_Sohini_Notebooks

Flask-ML-Pipeline_GCP-Tutorial

Explainable_End2End_Aug

Explainable Data Augmentation for Images

QU-net-Plus-Plus

tensorflow-object-detection-example

Deep-ReAP-for-Multi-pathology-Classification

Image_Annotation_Tools

Knowledge-transfer-for-ML

MaskRCNN_Medical_Image

Paralllel-ESN-For-Image-Quality

BioBERT_Named_Entity_Recognition

Computer_Vision_Transfer_Learning_and_Explainability

datasets

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

eCommerce_Demo

Finance_Anomaly_Detection

gke-deployment-testing-strategies

Hello-World

labelme

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

ml-deployment-on-gcloud

A pipeline to CI/CD of a machine learning model on Google Cloud Run

ML-Monitoring-for-Retinal-Images

Tensorflow-2-Object-Detection-API-Flask-Application

In this application we build an API endpoint for the Tensorflow Object Detection API and deploy it on Google Kubernetes Engine. The user hits the endpoint with image data and gets a response which consists of detections with scores, image data with overlay, image size (can be customized).

Tensorflow_hub_Webapps

What_If_Tool_Demo