- Zero-Cost Proxies for Lightweight NAS
- Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search
- LiteTransformerSearch: Training-free On-device Search for Efficient Autoregressive Language Models
- Training-free Transformer Architecture Search
- Training-Free Hardware-Aware Neural Architecture Search with Reinforcement Learning
- Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics
- A Feature Fusion Based Indicator for Training-Free Neural Architecture Search
- Neural Architecture Search without Training
- Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
- Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search
- A Training-Free Genetic Neural Architecture Search
- EcoNAS: Finding Proxies for Economical Neural Architecture Search
- EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search
- How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?
- FLASH: Fast Neural Architecture Search with Hardware Optimization
- Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
- Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
- EZNAS: Evolving Zero-Cost Proxies For Neural Architecture Scoring
- Zero-Cost Proxies Meet Differentiable Architecture Search
- Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
- Training-Free Multi-objective Evolutionary Neural Architecture Search via Neural Tangent Kernel and Number of Linear Regions
- Extensible Proxy for Efficient NAS
- ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients
This repo is designed to evaluate different zero-shot proxied for various benchmarks.
git clone https://github.com/google-research/nasbench
cd nasbench
In the nasbench
folder, you need to modify import tensorflow as tf
into import tensorflow.compat.v1 as tf
for the following files:
nasbench/api.py
nasbench/lib/evaluate.py
nasbench/lib/training_time.py
Then install nasbench
pip install -e .
Go to ~/dataset/img16/ImageNet16/
- Download ImageNet16-120:
gdown https://drive.google.com/uc?id=1vZe9VD0Sv5kTw-lR5lT-cmjBSh4AuLAH
Go to ~/dataset/nasbench/
:
- Download NASBench-101:
wget https://storage.googleapis.com/nasbench/nasbench_full.tfrecord
- Download NASBench-201:
gdown https://drive.google.com/uc?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_
Go to ~/dataset/nasbench/NATS
-
Download NATS-Bench-SSS:
gdown https://drive.google.com/uc?id=1scOMTUwcQhAMa_IMedp9lTzwmgqHLGgA
-
Optional (since NATS-Bench-TSS is same as NASBench-201):
- Download NATS-Bench-TSS:
gdown https://drive.google.com/uc?id=17_saCsj_krKjlCBLOJEpNtzPXArMCqxU
- Download NATS-Bench-TSS:
python main.py --searchspace=$SEARCH_SPACE --dataset=$DATASET --data_path=$PATH_OF_DATASET --metric=$PROXY_NAME