- PyTorch 1.0+
- NVIDIA-DALI (in development, not recommended)
Download the ImageNet dataset and move validation images to labeled subfolders. To do this, you can use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh
Taking MobileNetV2 1.0 as an example, pretrained models can be easily imported using the following lines and then finetuned for other vision tasks or utilized in resource-aware platforms.
from models.imagenet import mobilenetv2
net = mobilenetv2()
net.load_state_dict(torch.load('pretrained/mobilenetv2-c5e733a8.pth'))Configuration to reproduce our strong results efficiently, consuming around 2 days on 4x TiTan XP GPUs with non-distributed DataParallel and PyTorch dataloader.
- batch size 256
- epoch 150
- learning rate 0.05
- LR decay strategy cosine
- weight decay 0.00004
The newly released model achieves even higher accuracy, with larger bacth size (1024) on 8 GPUs, higher initial learning rate (0.4) and longer training epochs (250). In addition, a dropout layer with the dropout rate of 0.2 is inserted before the final FC layer, no weight decay is imposed on biases and BN layers and the learning rate ramps up from 0.1 to 0.4 in the first five training epochs.
python imagenet.py \
-a mobilenetv3 \
-d ../dataset \
--epochs 150 \
--lr-decay cos \
--lr 0.05 \
--wd 4e-5 \
-c <path-to-save-checkpoints> \
-j <num-workers>python imagenet.py \
-a mobilenetv3 \
-d ../dataset/ \
--weight ./checkpoint.pth.tar \
-eThe following is a BibTeX entry for the MobileNet V2 paper that you should cite if you use this model.
@InProceedings{Sandler_2018_CVPR,
author = {Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
title = {MobileNetV2: Inverted Residuals and Linear Bottlenecks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}
If you find this implementation helpful in your research, please also consider citing:
@InProceedings{Li_2019_ICCV,
author = {Li, Duo and Zhou, Aojun and Yao, Anbang},
title = {HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2019}
}
This repository is licensed under the Apache License 2.0.