README_AI_Applications_Codesets.md

Deep NeuralNet AI Applications Codesets

Following folders carry Jupyter notebook (interactive python notebook/ .ipynb) || Python Codeset as well as pseudocode || related Dockers, Dockerfile, docker build, nvidia-docker run time commands || refernce blogs from MEDIUM || Microservice for AI Model binary deployment as AI-as-a-Service for the models in Deep Learning/ Deep Reinforcement Learning/ Recommendation engine.

Image Object Detection SSD

• Object Detection- Computer Vision model Combining Cityscapes and Mapillary to train SSD using Supervise.ly

Image Segmentation UNET

• Image Segmentation- Computer Vision model Training road scene segmentation on Cityscapes with Tensorflow and UNet (medium)

Object Detection and Segmentation RCNN MaskRCNN

• Deep Dive 'Object Detection and Segmentation'

Skin Cancer Lesion detection from dermoscopic images

• Skin Cancer Lesion detection from dermoscopic images

Automated Brain Tumour Segmentation

• Automated_Brain_Tumour_Segmentation

Scene Text detection from wild Images using CRNN CTPN DenseNet-OCR

• CTPN Model for Scene Text Detection from wild Images

• DenseNet-OCR Model for Scene Text Detection

• CRNN Model for Scene Text Detection

CRNN == Convolutional Recurrent Neural Network CTPN == Connectionist Text Proposal Network DENSENET-OCR == DENSENET + CTC (Connectionist Temporal Classification loss) + CRNN (Convolutional Recurrent Neural Network )

Custom OCR Engine

• Custom OCR Engine/Model Plate number recognition with Keras (medium)

Text detection from IDCard

• Text detection from IDCard using CTPN

Automated Number Plate Detection

• Automated Number Plate Detection- Computer Vision model Number plate detection with Supervisely and Tensorflow (medium)

Microservice Microweb framework for AIaaS

• Microservice Microweb framework for AIaaS Using Flask Micro web framework to create Predict RESTful API service (AI-as-a-Service)