Sayak Paul

The repository is a collection of Colab Notebooks demonstrating non-trivial model conversion workflows in TensorFlow Lite.

In this repository, we provide tools to probe into the representations learned by different families of Vision Transformers (supervised pre-training with ImageNet-21k, ImageNet-1k, distillation, etc.)

This project shows how to serve an ONNX-optimized image classification model as a RESTful web service with FastAPI, Docker, and Kubernetes (k8s).

The repository consists of Jupyter Notebooks exploring different features of TensorFlow 2.

The project implements ConvNeXt models in TensorFlow and Keras and demonstrates their performance on the ImageNet-1k dataset. Additionally, it provides useful utility scripts.

The repository provides code to train an image classifier using distributed training within a containerized environment. We use AI Platform and Docker to achieve this.

This repository builds a product detection model to recognize products from grocery shelf images.

This project demonstrates the use of TensorFlow Object Detection API (along with GCP ML Engine) to automatically detect Red Blood Cells (RBCs), White Blood Cells (WBCs), and Platelets in each image taken via microscopic image readings.