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NAS-Bench-102.md
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# NAS-BENCH-102: Extending the Scope of Reproducible Neural Architecture Search
# [NAS-BENCH-102: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)
We propose an algorithm-agnostic NAS benchmark (NAS-Bench-102) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
The design of our search space is inspired from that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.
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We have tried our best to implement each method. However, still, some algorithms might obtain non-optimal results since their hyper-parameters might not fit our NAS-Bench-102.
If researchers can provide better results with different hyper-parameters, we are happy to update results according to the new experimental results. We also welcome more NAS algorithms to test on our dataset and would include them accordingly.
Note that you need to prepare the training and test data as described in [Preparation and Download](#preparation-and-download)
**Note that** you need to prepare the training and test data as described in [Preparation and Download](#preparation-and-download)
- [1] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V1.sh cifar10 -1`
- [2] `CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 -1`