Caffe-like explicit model constructor. C(onfig)Model

Related tags

Deep Learningcmodel
Overview

cmodel

Caffe-like explicit model constructor. C(onfig)Model

Installation

pip install git+https://github.com/bonlime/cmodel

Usage

In order to allow using your own modules you have to redefine CModel.eval after imports. Example:

from mypackage.modules import MyModule
from cmodel import CModel as OriginalCModel

class CModel(OriginalCModel):
    @staticmethod
    def eval(name):
        return eval(name)

# now you could use `MyModule` in your configs
Owner
Emil Zakirov. MIPT & Skoltech. Computer Vision Engineer.
Official implementation of Meta-StyleSpeech and StyleSpeech

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code

min95 168 Dec 28, 2022
Fast Neural Style for Image Style Transform by Pytorch

FastNeuralStyle by Pytorch Fast Neural Style for Image Style Transform by Pytorch This is famous Fast Neural Style of Paper Perceptual Losses for Real

Bengxy 81 Sep 03, 2022
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning

H-Transformer-1D Implementation of H-Transformer-1D, Transformer using hierarchical Attention for sequence learning with subquadratic costs. For now,

Phil Wang 123 Nov 17, 2022
A large-image collection explorer and fast classification tool

IMAX: Interactive Multi-image Analysis eXplorer This is an interactive tool for visualize and classify multiple images at a time. It written in Python

Matias Carrasco Kind 23 Dec 16, 2022
Unsupervised captioning - Code for Unsupervised Image Captioning

Unsupervised Image Captioning by Yang Feng, Lin Ma, Wei Liu, and Jiebo Luo Introduction Most image captioning models are trained using paired image-se

Yang Feng 207 Dec 24, 2022
Supervised forecasting of sequential data in Python.

Supervised forecasting of sequential data in Python. Intro Supervised forecasting is the machine learning task of making predictions for sequential da

The Alan Turing Institute 54 Nov 15, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
Collection of generative models in Pytorch version.

pytorch-generative-model-collections Original : [Tensorflow version] Pytorch implementation of various GANs. This repository was re-implemented with r

Hyeonwoo Kang 2.4k Dec 31, 2022
shufflev2-yolov5:lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size

pogg 1.5k Jan 05, 2023
2020 CCF大数据与计算智能大赛-非结构化商业文本信息中隐私信息识别-第7名方案

2020CCF-NER 2020 CCF大数据与计算智能大赛-非结构化商业文本信息中隐私信息识别-第7名方案 bert base + flat + crf + fgm + swa + pu learning策略 + clue数据集 = test1单模0.906 词向量

67 Oct 19, 2022
GNN-based Recommendation Benchmark

GRecX A Fair Benchmark for GNN-based Recommendation Homepage and Documentation Homepage: Documentation: Paper: GRecX: An Efficient and Unified Benchma

73 Oct 17, 2022
PyTorch implementation of SIFT descriptor

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can

Dmytro Mishkin 150 Dec 24, 2022
Single Image Deraining Using Bilateral Recurrent Network (TIP 2020)

Single Image Deraining Using Bilateral Recurrent Network Introduction Single image deraining has received considerable progress based on deep convolut

23 Aug 10, 2022
Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021)

EMI-FGSM This repository contains code to reproduce results from the paper: Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021) Xiaosen Wa

John Hopcroft Lab at HUST 10 Sep 26, 2022
Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders"

AAVAE Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders" Abstract Recent methods for self-supervised learnin

Grid AI Labs 48 Dec 12, 2022
[NeurIPS 2021] ORL: Unsupervised Object-Level Representation Learning from Scene Images

Unsupervised Object-Level Representation Learning from Scene Images This repository contains the official PyTorch implementation of the ORL algorithm

Jiahao Xie 55 Dec 03, 2022
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
Library for time-series-forecasting-as-a-service.

TIMEX TIMEX (referred in code as timexseries) is a framework for time-series-forecasting-as-a-service. Its main goal is to provide a simple and generi

Alessandro Falcetta 8 Jan 06, 2023
realsense d400 -> jpg + csv

Realsense-capture realsense d400 - jpg + csv Requirements RealSense sdk : Installation Python3 pyrealsense2 (RealSense SDK) Numpy OpenCV Tkinter Run

Ar-Ray 2 Mar 22, 2022
Multiple style transfer via variational autoencoder

ST-VAE Multiple style transfer via variational autoencoder By Zhi-Song Liu, Vicky Kalogeiton and Marie-Paule Cani This repo only provides simple testi

13 Oct 29, 2022