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HourNAS is an efficient neural architecture search method. Only using 3 hours (0.1 days) with one GPU, HourNAS can search an architecture that achieves a 77.0% Top-1 accuracy, which outperforms the state-of-the-art methods.
Paper: Zhaohui Yang, Yunhe Wang, Xinghao Chen, Jianyuan Guo, Wei Zhang, Chao Xu, Chunjing Xu, Dacheng Tao, Chang Xu. HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens. In CVPR 2021.
The overall network architecture of HourNAS is show below:
Dataset used: CIFAR-10
.HourNAS
├── README.md # descriptions about HourNAS
├── src
│ ├── architectures.py # definition of HourNAS-F model
│ ├── dataset.py # data preprocessing
│ ├── hournasnet.py # HourNAS general architecture
│ └── utils.py # utility functions
├── eval.py # evaluation interface
To Be Done
# infer example
python eval.py --model hournas_f_c10 --dataset_path [DATA_PATH] --GPU --ckpt [CHECKPOINT_PATH]
result: {'Top1-Acc': 0.9618389423076923} ckpt= ./hournas_f_cifar10.ckpt
| Model | FLOPs (M) | Params (M) | ImageNet Top-1 |
|---|---|---|---|
| MnasNet-A1 | 312 | 3.9 | 75.2% |
| HourNAS-E | 313 | 3.8 | 75.7% |
| EfficientNet-B0 | 390 | 5.3 | 76.8% |
| HourNAS-F | 383 | 5.3 | 77.0% |
More details in Paper.
We set the seed inside dataset.py.
Please check the official homepage.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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