NasNet-A-Mobile

This implements training of Res2Net101_v1b on the ImageNet dataset, mainly modified from pytorch/examples.

NasNet-A-Mobile Detail

Requirements

Install PyTorch (pytorch.org) pip install -r requirements.txt Note: pillow recommends installing a newer version. If the corresponding torchvision version cannot be installed directly, you can use the source code to install the corresponding version. The source code reference link: https://github.com/pytorch/vision， Suggestion the pillow is 9.1.0 and the torchvision is 0.6.0 Download the ImageNet dataset from http://www.image-net.org/ Then, and move validation images to labeled subfolders, using [the following shell script]

Training

To train a model, run main_npu_1p.py or main_npu_8p with the desired model architecture and the path to the ImageNet dataset:

O2 training 1p

bash scripts/train_1p.sh

O2 training 8p

bash scripts/train_8p.sh

O2 evaling 8p

bash scripts/eval_8p.sh

O2 online inference demo

source scripts/env_npu.sh python3 demo.py

O2 To ONNX

source scripts/set_npu_env.sh python3 pthtar2onnx.py

NasNet-A-Mobile training result

Acc@1	FPS	Npu_nums	Epochs	AMP_Type
-	320	1	1	O2
70.549	2839	8	240	O2

@misc{zoph2018learning, title={Learning Transferable Architectures for Scalable Image Recognition}, author={Barret Zoph and Vijay Vasudevan and Jonathon Shlens and Quoc V. Le}, year={2018}, eprint={1707.07012}, archivePrefix={arXiv}, primaryClass={cs.CV} }

公网地址说明

代码涉及公网地址参考 public_address_statement.md