Transfer Learning Python Packages

tensorflow-hub

A library for transfer learning by reusing parts of TensorFlow models.

autogluon-tabular

Fast and Accurate ML in 3 Lines of Code

autogluon-core

Fast and Accurate ML in 3 Lines of Code

autogluon-features

Fast and Accurate ML in 3 Lines of Code

autogluon-common

Fast and Accurate ML in 3 Lines of Code

autogluon-timeseries

Fast and Accurate ML in 3 Lines of Code

autogluon

Fast and Accurate ML in 3 Lines of Code

autogluon-multimodal

Fast and Accurate ML in 3 Lines of Code

torchxrayvision

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

autogluon-text

Fast and Accurate ML in 3 Lines of Code

autogluon-vision

Fast and Accurate ML in 3 Lines of Code

discrete-key-value-bottleneck-pytorch

Implementation of Discrete Key / Value Bottleneck, in Pytorch

autogluon-extra

Fast and Accurate ML in 3 Lines of Code

autogluon-mxnet

Fast and Accurate ML in 3 Lines of Code

tf-hub-nightly

A library for transfer learning by reusing parts of TensorFlow models.

carvekit

✂️ Automated high-quality background removal framework for an image using neural networks. ✂️

activations

AReLU: Attention-based-Rectified-Linear-Unit

autogluon-eda

Fast and Accurate ML in 3 Lines of Code

laserembeddings

LASER multilingual sentence embeddings as a pip package

sparsezoo

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

easytorch

EasyTorch is a research-oriented pytorch prototyping framework with a straightforward learning curve. It is highly robust and contains almost everything needed to perform any state-of-the-art experiments.

finetuner

:dart: Task-oriented embedding tuning for BERT, CLIP, etc.

sparseml

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

farm

:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.