Res2Net101_v1b
This implements training of Res2Net101_v1b on the ImageNet dataset, mainly modified from pytorch/examples.
Res2Net101_v1b 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: 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.py with the desired model architecture and the path to the ImageNet dataset:
1p training 1p
bash ./test/train_full_1p.sh --data_path=xxx # training accuracy
bash ./test/train_performance_1p.sh --data_path=xxx # training performance
8p training 8p
bash ./test/train_full_8p.sh --data_path=xxx # training accuracy
bash ./test/train_performance_8p.sh --data_path=xxx # training performance
eval default 8p， should support 1p
bash ./test/train_eval_8p.sh --data_path=xxx
O2 online inference demo
source scripts/set_npu_env.sh python3 demo.py
O2 To ONNX
source scripts/set_npu_env.sh python3 pthtar2onnx.py
Res2Net101_v1b training result
| Acc@1 | FPS | Npu_nums | Epochs | AMP_Type |
|---|---|---|---|---|
| - | 383.472 | 1 | 1 | O2 |
| 79.034 | 2611.314 | 8 | 100 | O2 |
Copyright 2020 Huawei Technologies Co., Ltd
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
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============================================================================
@article{gao2019res2net, title={Res2Net: A New Multi-scale Backbone Architecture}, author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}, journal={IEEE TPAMI}, year={2021}, doi={10.1109/TPAMI.2019.2938758}, }
公网地址说明
代码涉及公网地址参考 public_address_statement.md