Transfer Libraries

22 Components & Libraries

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Transfer Libraries

:mag: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

This command installs everything needed for basic Pipelines that use an in-memory DocumentStore. If you want to install only the dependencies needed for model inference on your local hardware (not re…

A blazing fast multi-language serialization framework powered by JIT and zero-copy.

In addition to cross-language serialization, Fury also features at: Different scenarios have different serialization requirements. Fury designed and implemented multiple binary protocols for those re…

A blazing fast multi-language serialization framework powered by jit and zero-copy

Transfer files over WiFi between your computer and your smartphone from the terminal

⚽📊 A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.

The README of this repository is a concise list of learning resources, data sources, libraries, papers, blogs, podcasts, etc., created by all those that have made contributions to the football analyt…

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem. ⭐ Star to support our work!

This is an open-source project welcoming your contributions. You can contribute in three ways: The development of PyKale is partially supported by the following project(s).

Reconstruction of the original paper on neural style transfer (Gatys et al.). I've additionally included reconstruction scripts which allow you to reconstruct only the content or the style of the image - for better understanding of how NST works.

The algorithm transfers style from one input image (the style image) onto another input image (the content image) using CNN nets (usually VGG-16/19) and gives a composite, stylized image out which ke…

Chest Xray image analysis using Deep learning !

Finally the saved models are used for making predictions.Confusion Matrix is used as the Performance Metrics for this classifcation task.

This is the open-source implement the paper "Color Transfer between Images" by Erik Reinhard, Michael Ashikhmin, Bruce Gooch and Peter Shirley.

Source / Target / Result

Transfer Learning Suite in Keras. Perform transfer learning using any built-in Keras image classification model easily!

All of the Keras built in models are made available: This project has the following dependencies: The only thing you have to do to get started is set up the folders in the following structure:

A python implementation of Deep-Image-Analogy based on pytorch.

python3 opencv3 I train a series of invert blocks which estimate the inputs given the outputs for each stage of Vgg19. The results are slightly worse than optimization-based method.

CNN image classifier implemented in Keras Notebook 🖼️.

Check out corresponding Medium article: Structure your data as follows: For binary classifications you are good to go! For non-binary classifications:

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a han…

Almost any Image classification problem using pytorch

To train the models: To test the models: total number of train images: 4268 total number of val images: 482 total number of test images: 794

Python package to transfer data in a fast, reliable, and packetized form

Note that you can specify many callbacks, but only one per packet ID

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

The solutions uploaded in this repository are only for reference when you got stuck somewhere. Please don't use these solutions to pass programming assignments.

style transfer web app [FastAPI + streamlit + Docker]

Three methods of color transfer implemented in python.

Three methods of color transfer implemented in Python.

pytorch-naive-video-neural-style-transfer

Create naive (no temporal loss) NST for videos with person segmentation. Just place your videos in data/, run and you get your stylized and segmented videos.

The first video of the series was created exactly using this method (I also used ReCoNet for 1 part of the video). On the left you can see typical NST output and the 2 other images on the right were …

An Implementation of Ebsynth for video stylization, and the original ebsynth for image stylization as an importable python library!

Perform things like style transfer, color transfer, inpainting, superimposition, video stylization and more! This implementation makes use of advanced physics based edge detection and RAFT optical fl…