Cat-or-dog | React-native drag-n-drop game | iOS library

by punksta JavaScript Version: 1.0.5 License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Cat-or-dog Summary

Cat-or-dog is a JavaScript library typically used in Telecommunications, Media, Telecom, Mobile, iOS, React Native, React applications. Cat-or-dog has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Mobile game where you need swipe photos of animals to match categories. I've made it just for fun and as some Animated api practice.

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Support

Cat-or-dog has a low active ecosystem.

It has 30 star(s) with 18 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

Cat-or-dog has no issues reported. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Cat-or-dog is 1.0.5

Quality

Cat-or-dog has 0 bugs and 0 code smells.

Security

Cat-or-dog has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Cat-or-dog code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Cat-or-dog is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Cat-or-dog releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed Cat-or-dog and discovered the below as its top functions. This is intended to give you an instant insight into Cat-or-dog implemented functionality, and help decide if they suit your requirements.

Get the random index of the given size .

Get all kandi verified functions for this library.

Cat-or-dog Key Features

No Key Features are available at this moment for Cat-or-dog.

Cat-or-dog Examples and Code Snippets

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Community Discussions

Trending Discussions on Cat-or-dog

Tensorflow.js Loaded Model Performing Significantly Worse than Keras Model

OpenCV - Python Bag Of Words(BoW) generating histograms from dictionary

QUESTION

Tensorflow.js Loaded Model Performing Significantly Worse than Keras Model

Asked 2019-Oct-13 at 14:46

So, I am attempting to create a Dog vs. Cat Image Classification model using Keras. Part of my goal is to create a website that deploys the model using Tensorflow.js. I have successfully deployed the model using Flask as the server.

The main issue is that the model is Tensorflow.js performs so much worse than the model in plain keras. When using plain keras, my model achieved around 90% accuracy on the test data. However, when used in tensorflow.js, the model did not get a single of the test images correct. I would appreciate any help or any tips on fixing this issue.

templates/index.html

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ANSWER

Answered 2019-Oct-13 at 14:46

After a ton of coffee and barely any sleep I came across a solution. Apparently, the interanals of WebGL works differently than the internals of Tensorflow in Python. The workaround here is to disable WebGL.

Just before you load the model of the graph, add...

Source https://stackoverflow.com/questions/58359811

QUESTION

OpenCV - Python Bag Of Words(BoW) generating histograms from dictionary

Asked 2019-Jan-23 at 10:41

I have been trying to create an image classifier in Python OpenCV 3.2.0 using keypoints and the bag of words technique. After some reading I found that I could peform this as follows

Extract image descriptors using AKAZE
Perform k-means clustering on the descriptors to generate the dictionary
Generate histograms of images based on dictionary
Train SVM using histograms

I managed to do steps 1 and 2 but have gotten stuck on steps 3 and 4.

I generated the histograms by using the labels returned by k-means clustering successfully (I think). However, when I wanted to use new test data that was not used to generate the dictionary I had some unexpected results. I tried to use a FLANN matcher like in this tutorial but the results I get from generating the histograms from the label data does not match the data returned from the FLANN matching.

I load up the images:

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ANSWER

Answered 2019-Jan-23 at 10:41

It seems that you cannot train a FlannBasedMatcher using a dictionary before hand as show below:

Source https://stackoverflow.com/questions/43104111

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