Jamison  Fisher

Jamison Fisher

1619363040

Boost Basic Dataset and Simple CNN to answer Real Environment Problem.

CNN — Leaf Classification with Data Augmentation, Background and Multi-Output.

1. Context

This project was built as part of the validation of our Data Scientist Bootcamp courses at DataScientest and put into practice everything that we have learnt during these 11 weeks of theoretical classes and ensure that every topic has been mastered.

The goal of this project is to localize and classify the species of a plant from a picture. Once the classification is done, return a description of the plant and identify an eventual disease.

#background #image-classification #cnn #deep-learning #tensorflow

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Boost Basic Dataset and Simple CNN to answer Real Environment Problem.
Jamison  Fisher

Jamison Fisher

1619363040

Boost Basic Dataset and Simple CNN to answer Real Environment Problem.

CNN — Leaf Classification with Data Augmentation, Background and Multi-Output.

1. Context

This project was built as part of the validation of our Data Scientist Bootcamp courses at DataScientest and put into practice everything that we have learnt during these 11 weeks of theoretical classes and ensure that every topic has been mastered.

The goal of this project is to localize and classify the species of a plant from a picture. Once the classification is done, return a description of the plant and identify an eventual disease.

#background #image-classification #cnn #deep-learning #tensorflow

Inside ABCD, A Dataset To Build In-Depth Task-Oriented Dialogue Systems

According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.

Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.

To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.

https://twitter.com/asapp/status/1397928363923177472

The dataset is currently available on GitHub.

#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets

Abdullah  Kozey

Abdullah Kozey

1658559780

Boost.GIL: Generic Image Library | Requires C++14 Since Boost 1.80

 

DocumentationGitHub ActionsAppVeyorAzure PipelinesRegressionCodecov
developGitHub ActionsAppVeyorAzuregilcodecov
masterGitHub ActionsAppVeyorAzuregilcodecov

Boost.GIL

Introduction

Boost.GIL is a part of the Boost C++ Libraries.

The Boost Generic Image Library (GIL) is a C++14 header-only library that abstracts image representations from algorithms and allows writing code that can work on a variety of images with performance similar to hand-writing for a specific image type.

Documentation

See RELEASES.md for release notes.

See CONTRIBUTING.md for instructions about how to build and run tests and examples using Boost.Build or CMake.

See example/README.md for GIL usage examples.

See example/b2/README.md for Boost.Build configuration examples.

See example/cmake/README.md for CMake configuration examples.

Requirements

The Boost Generic Image Library (GIL) requires:

  • C++14 compiler (GCC 6, clang 3.9, MSVC++ 14.1 (1910) or any later version)
  • Boost header-only libraries

Optionally, in order to build and run tests and examples:

  • Boost.Filesystem
  • Boost.Test
  • Headers and libraries of libjpeg, libpng, libtiff, libraw for the I/O extension and some of examples.

Branches

The official repository contains the following branches:

master This holds the most recent snapshot with code that is known to be stable.

develop This holds the most recent snapshot. It may contain unstable code.

Community

There is number of communication channels to ask questions and discuss Boost.GIL issues:

Contributing (We Need Your Help!)

If you would like to contribute to Boost.GIL, help us improve the library and maintain high quality, there is number of ways to do it.

If you would like to test the library, contribute new feature or a bug fix, see the CONTRIBUTING.md where the whole development infrastructure and the contributing workflow is explained in details.

You may consider performing code reviews on active pull requests or help with solving reported issues, especially those labelled with:

Any feedback from users and developers, even simple questions about how things work or why they were done a certain way, carries value and can be used to improve the library.

License

Distributed under the Boost Software License, Version 1.0.


Author:  boostorg
Source code: https://github.com/boostorg/gil
License: BSL-1.0 license

#cpluplus 

Create Your Own Real Image Dataset with python (Deep Learning)

We have all worked with famous Datasets like CIFAR10 , MNIST , MNIST-fashion , CIFAR100, ImageNet and more. But , what about working on projects with custom made datasets according to your own needs. This also essentially makes you a complete master when it comes to handling image data

most of us probably know how to handle and store numerical and categorical data in csv files. But, the idea of storing Image data in files is very uncommon. Having said that , let’s see how to make our own image dataset with python

Code Begins Here :

1)Let’s start by importing the necessary libraries

#importing the libraries
import os 
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
  1. Then , we need to set the path to the folder or directory that contains the image files. Here, the pictures that I need to upload are being stored in the path mentioned below
#setting the path to the directory containing the pics
path = '/media/ashwinhprasad/secondpart/pics'

#image-dataset #machine-learning-datasets #own-image-dataset #real-data #deep learning

Bella Garvin

Bella Garvin

1624088381

Real Estate App Development I Real Estate Software Development USA

Orbit Edge is a top real-estate app development company that provides top-quality real estate software and app development solutions that facilitates the realtors, builder and other property brokers. Time-saving and cost-saving real estate software solutions help enterprises to sustain themselves in the real estate market.

#real estate app development company #real estate website development #real estate app development services #real estate app development #real estate software development company