This repositary contain all my exercises and projects of Udacity Computer Vision Nanodegree Program

Jupyter Notebook 97.45% Python 0.40% Lua 0.05% MATLAB 0.14% C++ 0.05% C 0.04% Makefile 0.01% HTML 1.87%

udacity-nanodegree udacity python3 pytorch lstm-neural-networks cnn-for-visual-recognition cnn-rnn landmark-detection facial-keypoint-detection facial-detection

udacity-computer-vision-nanodegree-program's Introduction

Computer Vision Nanodegree

This repository contains my exercises and projects for the Computer-Vision-Nanodegree at Udacity.

Created by Saurav Raghuvanshi

This repositary contain all my exercises and projects of Udacity Computer Vision Nanodegree Program

Project 1: Facial Keypoint Detection

Facial Keypoint Detection Project

In this project, I build a facial keypoint detection system. The system consists of a face detector that uses Haar Cascades and a Convolutional Neural Network (CNN) that predict the facial keypoints in the detected faces. The facial keypoint detection system takes in any image with faces and predicts the location of 68 distinguishing keypoints on each face.

Some results from my facial keypoint detection system:

The Udacity repository for this project: P1_Facial_Keypoints

Project 2: Image Captioning

Image Captioning Project

In this project, I design and train a CNN-RNN (Convolutional Neural Network - Recurrent Neural Network) model for automatically generating image captions. The network is trained on the Microsoft Common Objects in COntext (MS COCO) dataset. The image captioning model is displayed below.

Image source

One good and one not so good sample made by my model:

The Udacity repository for this project: CVND---Image-Captioning-Project

Project 3: Landmark Detection

Landmark Detection Project

In this project, I implement SLAM (Simultaneous Localization and Mapping) for a 2-dimensional world. Sensor and motion data gathered by a simulated robot is used to create a map of an environment. SLAM gives us a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, etc.