few-shot | Repository for few-shot learning machine learning projects | Machine Learning library

by oscarknagg Python Version: Current License: MIT

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

kandi X-RAY | few-shot Summary

few-shot is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. few-shot has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Repository for few-shot learning machine learning projects

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Quality

Security

License

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Support

few-shot has a low active ecosystem.

It has 772 star(s) with 159 fork(s). There are 15 watchers for this library.

It had no major release in the last 6 months.

There are 24 open issues and 11 have been closed. On average issues are closed in 11 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of few-shot is current.

Quality

few-shot has 0 bugs and 0 code smells.

Security

few-shot has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

few-shot code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

few-shot 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

few-shot releases are not available. You will need to build from source code and install.

Build file is available. You can 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 few-shot and discovered the below as its top functions. This is intended to give you an instant insight into few-shot implemented functionality, and help decide if they suit your requirements.

Embed a matching net episode
Computes the pairwise distances between two matrices
Create a label tensorflow task label
Fit a model
Invoke callback on each batch
Calls on_batch_end
Run on each epoch
Evaluate a model
Compute the k - th prototypes for each class
Calculate loss for each epoch
Compute the categorical accuracy
Create a function to prepare a nshot task
Updates the optimizer with the given parameters
Reduce learning rate
Create a function that prepares a meta - batch
Generate all the characters in a folder
Generate all glyphs for each character
Create directory
Removes a directory
Return a n - shot encoder
Create a block of convolutional convolutional layer
Apply functional convolution
Setup log files
Calculate learning rate
Evaluate the model
Initialize the optimiser

Get all kandi verified functions for this library.

few-shot Key Features

No Key Features are available at this moment for few-shot.

few-shot Examples and Code Snippets

No Code Snippets are available at this moment for few-shot.

Community Discussions

Trending Discussions on few-shot

How to use a customized dataset for training with PyTorch/few-shot-vid2vid

When I run deep learning training code on Google Colab, do the resulting weights and biases get saved somewhere?

QUESTION

How to use a customized dataset for training with PyTorch/few-shot-vid2vid

Asked 2020-Mar-03 at 01:13

I’d like to use my own dataset created from the FaceForensics footage with few-show-vid2vid. So I generated image sequences with ffmpeg and keypoints with dlib. When I try to start the training script, I get the following error. What exactly is the problem? The provided small dataset was working for me.

...

ANSWER

Answered 2020-Mar-03 at 01:13

for i in range(67):

This is incorrect, you should be using range(68) for 68 face landmarks. You can verify this with python -c "for i in range(67): print(i)" which will only count from 0 to 66 (67 total numbers). python -c "for i in range(68): print(i)" will count from 0 to 67 (68 items) and get the whole face landmark set.

Source https://stackoverflow.com/questions/60373136

QUESTION

When I run deep learning training code on Google Colab, do the resulting weights and biases get saved somewhere?

Asked 2020-Jan-07 at 15:38

I am training some deep learning code from this repository on a Google Colab notebook. The training is ongoing and seems like it is going to take a day or two.

I am new to deep learning, but my question:

Once the Google Colab notebook has finished running the training script, does this mean that the resulting weights and biases will be hard written to a model somewhere (in the repository folder that I have on my Google Drive), and therefore I can then run the code on any test data I like at any point in the future? Or, once I close the Google Colab notebook, do I lose the weight and bias information and would have to run the training script again if I wanted to use the neural network?

I realise that this might depend on the details of the script (again, the repository is here), but I thought that there might be a general way that these things work also.

Any help in understanding would be greatly appreciated.

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ANSWER

Answered 2020-Jan-07 at 15:31

No; Colab comes with no built-in checkpointing; any saving must be done by the user - so unless the repository code does so, it's up to you.

Note that the repo would need to figure out how to connect to a remote server (or connect to your local device) for data transfer; skimming through its train.py, there's no such thing.

How to save model? See this SO; for a minimal version - the most common, and a reliable option is to "mount" your Google Drive onto Colab, and point save/load paths to direct

Source https://stackoverflow.com/questions/59631255

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install few-shot

You can download it from GitHub.
You can use few-shot 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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