"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

Overview

NAS-Bench-301

This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

The surrogate models can be downloaded on figshare. This includes the models for v0.9 and v1.0 as well as the dataset that was used to train the surrogate models. We also provide the full training logs for all architectures, which include learning curves on the train, validation and test sets. These can be automatically downloaded, please see nasbench301/example.py.

To install all requirements (this may take a few minutes), run

$ cat requirements.txt | xargs -n 1 -L 1 pip install
$ pip install nasbench301

If installing directly from github

$ git clone https://github.com/automl/nasbench301
$ cd nasbench301
$ cat requirements.txt | xargs -n 1 -L 1 pip install
$ pip install .

To run the example

$ python3 nasbench301/example.py

To fit a surrogate model run

$ python3 fit_model.py --model gnn_gin --nasbench_data PATH_TO_NB_301_DATA_ROOT --data_config_path configs/data_configs/nb_301.json  --log_dir LOG_DIR

NOTE: This codebase is still subject to changes. Upcoming updates include improved versions of the surrogate models and code for all experiments from the paper. The API may still be subject to changes.

Owner
AutoML-Freiburg-Hannover
AutoML-Freiburg-Hannover
Submanifold sparse convolutional networks

Submanifold Sparse Convolutional Networks This is the PyTorch library for training Submanifold Sparse Convolutional Networks. Spatial sparsity This li

Facebook Research 1.8k Jan 06, 2023
COCO Style Dataset Generator GUI

A simple GUI-based COCO-style JSON Polygon masks' annotation tool to facilitate quick and efficient crowd-sourced generation of annotation masks and bounding boxes. Optionally, one could choose to us

Hans Krupakar 142 Dec 09, 2022
[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️⚠️

Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples This repository is the official implementation of "Tow

Sungyoon Lee 4 Jul 12, 2022
공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다.

ObsCare_Main 소개 공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다. CCTV의 대수가 급격히 늘어나면서 관리와 효율성 문제와 더불어, 곳곳에 설치된 CCTV를 개별 관제하는 것으로는 응급 상

5 Jul 07, 2022
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.

CyberBattleSim April 8th, 2021: See the announcement on the Microsoft Security Blog. CyberBattleSim is an experimentation research platform to investi

Microsoft 1.5k Dec 25, 2022
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble

datasketch: Big Data Looks Small datasketch gives you probabilistic data structures that can process and search very large amount of data super fast,

Eric Zhu 1.9k Jan 07, 2023
AI assistant built in python.the features are it can display time,say weather,open-google,youtube,instagram.

AI assistant built in python.the features are it can display time,say weather,open-google,youtube,instagram.

AK-Shanmugananthan 1 Nov 29, 2021
Official implementation of the NRNS paper: No RL, No Simulation: Learning to Navigate without Navigating

No RL No Simulation (NRNS) Official implementation of the NRNS paper: No RL, No Simulation: Learning to Navigate without Navigating NRNS is a heriarch

Meera Hahn 20 Nov 29, 2022
Code repository for "Free View Synthesis", ECCV 2020.

Free View Synthesis Code repository for "Free View Synthesis", ECCV 2020. Setup Install the following Python packages in your Python environment - num

Intelligent Systems Lab Org 253 Dec 07, 2022
MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving Code will be available soon. Motivation Architecture

Kai Chen 24 Apr 19, 2022
Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations

Facebook Research 75 Dec 19, 2022
A python library for face detection and features extraction based on mediapipe library

FaceAnalyzer A python library for face detection and features extraction based on mediapipe library Introduction FaceAnalyzer is a library based on me

Saifeddine ALOUI 14 Dec 30, 2022
Politecnico of Turin Thesis: "Implementation and Evaluation of an Educational Chatbot based on NLP Techniques"

THESIS_CAIRONE_FIORENTINO Politecnico of Turin Thesis: "Implementation and Evaluation of an Educational Chatbot based on NLP Techniques" GENERATE TOKE

cairone_fiorentino97 1 Dec 10, 2021
GRF: Learning a General Radiance Field for 3D Representation and Rendering

GRF: Learning a General Radiance Field for 3D Representation and Rendering [Paper] [Video] GRF: Learning a General Radiance Field for 3D Representatio

Alex Trevithick 243 Dec 29, 2022
BboxToolkit is a tiny library of special bounding boxes.

BboxToolkit is a light codebase collecting some practical functions for the special-shape detection, such as oriented detection

jbwang1997 73 Jan 01, 2023
SegNet including indices pooling for Semantic Segmentation with tensorflow and keras

SegNet SegNet is a model of semantic segmentation based on Fully Comvolutional Network. This repository contains the implementation of learning and te

Yuta Kamikawa 172 Dec 23, 2022
TensorFlow-based neural network library

Sonnet Documentation | Examples Sonnet is a library built on top of TensorFlow 2 designed to provide simple, composable abstractions for machine learn

DeepMind 9.5k Jan 07, 2023
Code of TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

TVT Code of TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation Datasets: Digit: MNIST, SVHN, USPS Object: Office, Office-Home, Vi

37 Dec 15, 2022
Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)

Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021) Single-cause Perturbation (SCP) is a framework to estimate the m

Zhaozhi Qian 9 Sep 28, 2022
PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021.

IBRNet: Learning Multi-View Image-Based Rendering PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021. IBRN

Google Interns 371 Jan 03, 2023