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actor-amazon-crawler

Amazon crawler - this configuration will extract items for a keywords that you will specify in the input, and it will automatically extract all pages for the given keyword. You can specify more keywords on the input for one run.

ad-library-api-script-repository

GitHub repository of commonly used python scripts that allows everyone to pull data via the Ad Library API

advance-ecommerce-in-laravel-7

Advance e-commerce website in laravel 7.

amazon-scraper

A simple web scraper to extract Product Data and Pricing from Amazon

angular

animatedsite

ar-episode1

Rebuilding awwwards websites using react and gsap.

ar.js

Image tracking, Location Based AR, Marker tracking. All on the Web.

arfoundation-samples

Example content for Unity projects based on AR Foundation

arlocation

avilio

blog

blog2

braintemp

businesscardar

contentful-ecom

corona-tracker

cosmetics-ecom

deepfashion2

DeepFashion2 Dataset https://arxiv.org/pdf/1901.07973.pdf

drdabbas

ecomcontentfulcms

ecommercecrawlers

实战🐍多种网站、电商数据爬虫🕷。包含🕸：淘宝商品、微信公众号、大众点评、企查查、招聘网站、闲鱼、阿里任务、博客园、微博、百度贴吧、豆瓣电影、包图网、全景网、豆瓣音乐、某省药监局、搜狐新闻、机器学习文本采集、fofa资产采集、汽车之家、国家统计局、百度关键词收录数、蜘蛛泛目录、今日头条、豆瓣影评、携程、小米应用商店、安居客、途家民宿❤️❤️❤️。微信爬虫展示项目:

egjs-infinitegrid

A module used to arrange card elements including content infinitely on a grid layout.

face-api.js

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

face-detection-javascript

face_recognition

The world's simplest facial recognition api for Python and the command line

fashion-class-classification-python

How to Develop a Deep CNN for Fashion-MNIST Clothing Classification by Jason Brownlee on May 10, 2019 in Deep Learning for Computer Vision Tweet Share Last Updated on October 3, 2019 The Fashion-MNIST clothing classification problem is a new standard dataset used in computer vision and deep learning. Although the dataset is relatively simple, it can be used as the basis for learning and practicing how to develop, evaluate, and use deep convolutional neural networks for image classification from scratch. This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how to save the model and later load it to make predictions on new data. In this tutorial, you will discover how to develop a convolutional neural network for clothing classification from scratch. After completing this tutorial, you will know: How to develop a test harness to develop a robust evaluation of a model and establish a baseline of performance for a classification task. How to explore extensions to a baseline model to improve learning and model capacity. How to develop a finalized model, evaluate the performance of the final model, and use it to make predictions on new images. Discover how to build models for photo classification, object detection, face recognition, and more in my new computer vision book, with 30 step-by-step tutorials and full source code. Let’s get started. Update Jun/2019: Fixed minor bug where the model was defined outside of the CV loop. Updated results (thanks Aditya). Updated Oct/2019: Updated for Keras 2.3 and TensorFlow 2.0. How to Develop a Deep Convolutional Neural Network From Scratch for Fashion MNIST Clothing Classification How to Develop a Deep Convolutional Neural Network From Scratch for Fashion MNIST Clothing Classification Photo by Zdrovit Skurcz, some rights reserved. Tutorial Overview This tutorial is divided into five parts; they are: Fashion MNIST Clothing Classification Model Evaluation Methodology How to Develop a Baseline Model How to Develop an Improved Model How to Finalize the Model and Make Predictions Want Results with Deep Learning for Computer Vision? Take my free 7-day email crash course now (with sample code). Click to sign-up and also get a free PDF Ebook version of the course. Download Your FREE Mini-Course Fashion MNIST Clothing Classification The Fashion-MNIST dataset is proposed as a more challenging replacement dataset for the MNIST dataset. It is a dataset comprised of 60,000 small square 28×28 pixel grayscale images of items of 10 types of clothing, such as shoes, t-shirts, dresses, and more. The mapping of all 0-9 integers to class labels is listed below. 0: T-shirt/top 1: Trouser 2: Pullover 3: Dress 4: Coat 5: Sandal 6: Shirt 7: Sneaker 8: Bag 9: Ankle boot It is a more challenging classification problem than MNIST and top results are achieved by deep learning convolutional neural networks with a classification accuracy of about 90% to 95% on the hold out test dataset.

firegram

Starting & final course files for the FireGram tutorial (React, Firebase & Framer Motion) on Brad Traversy's YouTube channel.

fit1-master

This is Taxi-booking-mobile app using React Native (Expo)