FoodBinaryClassification | few_shot_learning_in_binary_classicfication

by Theohhhu Python Version: Current License: MIT

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

kandi X-RAY | FoodBinaryClassification Summary

FoodBinaryClassification is a Python library. FoodBinaryClassification has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However FoodBinaryClassification build file is not available. You can download it from GitHub.

few_shot_learning_in_binary_classicfication

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FoodBinaryClassification has a low active ecosystem.

It has 2 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

FoodBinaryClassification has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FoodBinaryClassification is current.

Quality

FoodBinaryClassification has no bugs reported.

Security

FoodBinaryClassification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

FoodBinaryClassification 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

FoodBinaryClassification releases are not available. You will need to build from source code and install.

FoodBinaryClassification has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Get argument parser .
Calculate the loss .
Iterate over the training data .
Find all files in a given directory
get the path label and target
Gets the list of all available classes .
Load an image .
Create a convolutional block .
Initialize data loader .
Find the index of the classes .

Get all kandi verified functions for this library.

FoodBinaryClassification Key Features

No Key Features are available at this moment for FoodBinaryClassification.

FoodBinaryClassification Examples and Code Snippets

No Code Snippets are available at this moment for FoodBinaryClassification.

Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install FoodBinaryClassification

You can download it from GitHub.
You can use FoodBinaryClassification like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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although without sufficient training, out NN can quickly separate the new kind fruit via the few-shot learning efficiently. 与原始论文不同，在固定分类状态个数的问题中，我们的n ways 并不是起到标签作用，而是起到增加计算效率以及类似于momentum的效果，理论上来说，当n=训练集全部类时，few-shot-learning的效率最高梯度下降方向最具有代表性，但是受制于硬件，我们只采用n=1-10。相较于标准的few-shot-learning模型，我们的模型收敛于更为固定的分类点，同时拉大同类水果不同状态的特征。.

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