OpenMMLab Model Compression Toolbox and Benchmark.
PyTorch to Keras model convertor
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Automatic scoring system for steel darts using OpenCV, a Raspberry Pi 3 Model B and two webcams.
DeepDarts is the first deep learning-based automatic scoring system for steel-tip darts. It predicts dart scores from a single image taken from any camera angle.
Differentiable architecture search for convolutional and recurrent networks
[AAAI '23] PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor
PC-DARTS (PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search, published in ICLR 2020) implemented in Tensorflow 2.0+. This is an unofficial implementation.
Pure python implementation of DARTS (Double ARray Trie System)
New product demand forecasting via Content based learning for multi-branch stores: Ali and Nino Use Case
Code for Cross-Task Neural Architecture Search for EEG Signals
[IROS 2021] ADD: A Fine-grained Dynamic Inference Architecture for Semantic Image Segmentation
Open-source preprocessing tool that can create, at the required level of accuracy, a fully conformal uniformly distributed grid for a given realistic fracture network. This leads to a robust way of constructing a hierarchy of Discrete-Fracture-Models for uncertainty quantification of energy production from reservoirs with natural fracture networ…
The DeepTSF time series forecasting repository developed by ICCS within the I-NERGY H2020 project
MC-DARTS : Model Size Constrained Differentiable Architecture Search
As a toy project, this repository provides implementations of the first order Differentiable Architecture Search (DARTS) on the fashion-mnist dataset in three different frameworks.
Modeling multiphase flow in fractured porous media using DARTS: a simple DFM example. See https://darts.citg.tudelft.nl/ for more information
PyTorch image classification models pre-trained
A Python3 assistant for playing darts. No dependencies.
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