This repo is the official implementation with MXNet/Gluon for:
Video presentation on YouTube:
- 2021.11.27 thanks PaddlePaddle's implementation
- 2021.08.18 release MXNet trained
weight&&log.txt&&mac.txt - 2021.07.06
pytorchversion released
- Python >= 3.6
- MXNet >= 1.5.1
- GluonCV >= 0.7.0
- mxboard >= 0.1.0
- scipy
- tqdm
- portalocker
├── README.md
├── models
│ ├── asymmnet.py
│ ├── common.py
│ ├── ghostnet.py
│ ├── mobilenetv2.py
│ └── mobilenetv3.py
├── utils
│ ├── args_helper.py
│ ├── utils.py
│ └── visualization.py
└── train.py
Referred gluoncv_examples_classification
For training ImageNet
- First, prepare data recordfile for ImageNet.
- Training
export MXNET_SAFE_ACCUMULATION=1 && export MXNET_ENABLE_GPU_P2P=1
python train.py \
--dataset imagenet \
--rec-train /path/to/train.rec \
--rec-train-idx /path/to/train.idx \
--rec-val /path/to/val.rec \
--rec-val-idx /path/to/val.idx \
--batch-size 256 --model asymmnet_large \
--mode hybrid --wd 0.00003 \
--lr 2.6 --lr-mode cosine --num-epochs 360 --num-gpus 8 -j 48 \
--warmup-epochs 5 --no-wd --last-gamma \
--label-smoothing --width-scale 1.0 \
--dtype float16 --dropout 0.1 \
--tag-name 1.0x-fp16-8gpu-lr2.6-360e-drop0.1 \
--log-interval 100
@InProceedings{Yang_2021_CVPR,
author = {Yang, Haojin and Shen, Zhen and Zhao, Yucheng},
title = {AsymmNet: Towards Ultralight Convolution Neural Networks Using Asymmetrical Bottlenecks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021},
pages = {2339-2348}
}