qubvel

segmentation_models.pytorch

Segmentation models with pretrained backbones. PyTorch.

segmentation_models

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

efficientnet

Implementation of EfficientNet model. Keras and TensorFlow Keras.

classification_models

Classification models trained on ImageNet. Keras.

ttach

Image Test Time Augmentation with PyTorch!

tta_wrapper

Test Time image Augmentation (TTA) wrapper for Keras model.

open-cities-challenge

First place solution for Open Cities DrivenData challenge

segmentation-models-pytorch

EfficientNet-PyTorch

A PyTorch implementation of EfficientNet

keras_lr_finder

Plots the change of the loss function of a Keras model when the learning rate is exponentially increasing.

qubvel

template

Template for data science project based on Docker and Jupyter server with Keras/Pytorch

volumentations

Library for 3D augmentations

albumentations

fast image augmentation library and easy to use wrapper around other libraries

ipyvolume

3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL

dvc-smp-test

vision

Datasets, Transforms and Models specific to Computer Vision

catalyst

Reproducible and fast DL & RL.

lightning-hydra-template

PyTorch Lightning + Hydra. A feature-rich template for rapid, scalable and reproducible ML experimentation with best practices. ⚡🔥⚡

notebooks

Notebooks using the Hugging Face libraries 🤗

tpu

Reference models and tools for Cloud TPUs.

blog

Public repo for HF blog posts

catalyst_sphinx_theme

docker-python

Kaggle Python docker image

DocRes

[CVPR 2024] DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks

evaluate

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

overpass-api-python-wrapper

Python bindings for the OpenStreetMap Overpass API

silero-vad

Silero VAD: pre-trained enterprise-grade Voice Activity Detector

streamlit

Streamlit — The fastest way to build data apps in Python

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.