Pavel Iakubovskii (qubvel)

qubvel

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Company:Denti.AI

Location:Moscow

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Pavel Iakubovskii's repositories

segmentation_models.pytorch

Segmentation models with pretrained backbones. PyTorch.

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segmentation_models

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

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efficientnet

Implementation of EfficientNet model. Keras and TensorFlow Keras.

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classification_models

Classification models trained on ImageNet. Keras.

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ttach

Image Test Time Augmentation with PyTorch!

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tta_wrapper

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

open-cities-challenge

First place solution for Open Cities DrivenData challenge

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keras_telegram_callback

Telegram-bot callback for your Keras model

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keras_lr_finder

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

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EfficientNet-PyTorch

A PyTorch implementation of EfficientNet

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keras-applications

Reference implementations of popular deep learning models.

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template

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

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albumentations

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

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MMdnn

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

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volumentations

Library for 3D augmentations

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vision

Datasets, Transforms and Models specific to Computer Vision

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catalyst

Reproducible and fast DL & RL.

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ipyvolume

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

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keras

Deep Learning for humans

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tpu

Reference models and tools for Cloud TPUs.

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docker-python

Kaggle Python docker image

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lightning-hydra-template

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

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overpass-api-python-wrapper

Python bindings for the OpenStreetMap Overpass API

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streamlit

Streamlit — The fastest way to build data apps in Python

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