yaelbenshalom/objects-recognition-and-classification

Objects recognition and classification using machine learning, computer vision and real-time object detection algorithm

Objects Recognition and Classification

Independent Project (ME499), Northwestern University, Winter 2021.
Take a look at my portfolio post for more information about the project.

Table of Contents

• Project Overview
  • Traffic sign detection and classification
  • Trash detection, classification, and segmentation
  • Recycling Baxter Implementation

Project Overview

This project contains 2 modules:

Traffic sign detection and classification

In this module I built and trained a neural network to detect and classify different traffic signs using PyTorch, OpenCV and YoloV3.
This module contained 2 parts:

• Traffic sign classification - Were I build and trained a neural network to classify different traffic-signs images.
• Traffic sign detection - Were I trained neural network to detect different traffic-signs in an image/video.

An example of traffic detection program output:

Trash detection, classification, and segmentation

In this module I built and trained a neural network to detect and classify different traffic signs using PyTorch, OpenCV and YoloV5.
This module contained 2 parts:

• Trash classification - Were I build and trained a neural network to classify different recyclable objects' images.
• Trash detection - Were I trained neural network to detect different recyclable objects in an image/video.

An example of trash detection program output:

Recycling Baxter Implementation

As an additional development of the Recycler Baxter project, I used the ML algorithm I implemented in this project to detect and locate the recyclable objects sorted by the baxter robot:

objects-recognition-and-classification's Issues

Issue in accessing the files

the files are not downloadable

taco10.yaml

I can not find taco10.yaml... please help me

Many files are missing for the video recognition :')

These lines require files which is not in the repo and there is no explanation of how to get them.

1- with open("data/mean_image_rgb.pickle", 'rb') as f:
2- weights_path = "weights/yolov3.weights"
3- config_path = "weights/yolov3.cfg"