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ad-versarial

aerial_pedestrian_detection

agile-6363

A simple question bank application

awesome-vision-and-language

A curated list of awesome vision and language resources (still under construction... stay tuned!)

best-readme-template

An awesome README template to jumpstart your projects!

big-companies-interview-questions

A curated list of previous asked Interview Question at Big Companies and Startups 🤲 🏆

caffe2

Caffe2 is a lightweight, modular, and scalable deep learning framework.

classifyte

controlled-transfer-learning

cs131_notes

Class notes for CS 131.

darknet

Convolutional Neural Networks

deep-residual-networks

Deep Residual Learning for Image Recognition

deepsegmentor

A Pytorch implementation of DeepCrack and RoadNet projects.

foolbox

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

google-images-download

Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!

igarss2022_leveecrackdetection

image_segmentation

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

iterlunet

Codebase to train, evaluate and compare image segmentation models

keras-cv

Industry-strength Computer Vision workflows with Keras

keras_object_detection

Convert any classification model or architecture trained in keras to an object detection model

ladi-tutorial

Tutorial for the Low Altitude Disaster Imagery (LADI) dataset

manisa.github.io

max-object-detector

Localize and identify multiple objects in a single image.

neural-fly-for-flood-control-systems

Public facing code for Neural Fly

nlp-with-python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

personal-website

Code that'll help you kickstart a personal website that showcases your work as a software developer.

plotneuralnet

Latex code for making neural networks diagrams

presentation2.0

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

sandboilnet

This repository contains codebase and links for datasets of our paper based on controlled transfer learning.