Few-Shot | PyTorch implementation of a few shot , and meta-learning | Computer Vision library

by Shandilya21 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Few-Shot Summary

Few-Shot is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Computer Vision, 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.

A PyTorch implementation of a few shot, and meta-learning algorithms for image classification.

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Quality

Security

License

Reuse

Support

Few-Shot has a low active ecosystem.

It has 12 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 3 days. There are 4 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 no bugs reported.

Security

Few-Shot has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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.

Calculates the gradients of the meta - model
Create task label label
Apply functional
Creates a function that returns the gradient of a given parameter
Fit a model
Invoke callback on each batch
Invokes callback for each callbacks
Run on each epoch
Compute the prototypes of a model
Compute the pairwise distances between two matrices
Compute the k - th prototypes for each class
Calculate loss for each epoch
Compute the categorical accuracy
Calculate the learning rate for a given epoch
Reduce the learning rate of a given epoch
Create a function to prepare a nshot task
Create a function to prepare a meta - batch
Returns a n - shot encoder
Create a convolutional block
Create directory
Removes a directory
Setup log files
Schedule learning rate for a given epoch
Set learning rate
Evaluate the model
Initialize the optimizer

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?

KeyError: 'class_name' in python3.7/site-packages/pandas/core/indexes/base.py

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.

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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

QUESTION

KeyError: 'class_name' in python3.7/site-packages/pandas/core/indexes/base.py

Asked 2019-Nov-30 at 05:49

I am trying to use one Github repo and I get the following error in python source files. I looked at posts like [this][1] but couldn't figure the exact problem.

Here's the error that I see:

...

ANSWER

Answered 2019-Nov-30 at 05:05

The error is due to the way you are handling unique values (self.unique_characters), particulary at df['class_name']. This chunk is looking for a column named class_name, and you clearly don't have such a column. Instead, I believe you can achieve your goal as follows:

Source https://stackoverflow.com/questions/59111430

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

Contributions are very welcome. If you know how to make this code better, please open an issue. If you want to submit a pull request, please open an issue first.

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