This is a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results.
This repository aims to accelarate the advance of Deep Learning Research, make reproducible results and easier for doing researches, and in Pytorch.
- SENet: Squeeze-and-excitation Networks (paper)
- SKNet: Selective Kernel Networks (paper)
- CBAM: Convolutional Block Attention Module (paper)
- GCNet: GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond (paper)
- BAM: Bottleneck Attention Module (paper)
- SGENet: Spatial Group-wise Enhance: Enhancing Semantic Feature Learning in Convolutional Networks (paper)
Single crop validation error on ImageNet-1k (center 224x224/320x320 crop from resized image with shorter side = 256).
classifiaction training settings |
---|
RandomResizedCrop, RandomHorizontalFlip |
0.1 init lr, total 100 epochs, decay at every 30 epochs |
sync SGD, naive softmax cross entropy loss, 1e-4 weight decay, 0.9 momentum |
8 gpus, 32 images per gpu |
Model | #P | GFLOPs | Top-1 Acc | Top-5 Acc | Download | log |
---|---|---|---|---|---|---|
ResNet50 | 25.56M | 4.122 | 76.3840 | 92.9080 | BaiduDrive(zuvx) | old_resnet50.log |
Oct-ResNet50 (0.125) | ||||||
SE-ResNet50 | 28.09M | 4.130 | 77.1840 | 93.6720 | ||
SK-ResNet50 | 26.15M | 4.185 | 77.5380 | 93.7000 | BaiduDrive(tfwn) | sk_resnet50.log |
BAM-ResNet50 | 25.92M | 4.205 | 76.8980 | 93.4020 | ||
CBAM-ResNet50 | 28.09M | 4.139 | 77.6260 | 93.6600 | BaiduDrive(bram) | cbam_resnet50.log |
GC-ResNet50 | ||||||
SGE-ResNet50 | 25.56M | 4.127 | 77.5840 | 93.6640 | BaiduDrive(gxo9) | sge_resnet50.log |
ResNet101 | 44.55M | 7.849 | 78.2000 | 93.9060 | BaiduDrive(js5t) | old_resnet101.log |
Oct-ResNet101 (0.125) | ||||||
SE-ResNet101 | 49.33M | 7.863 | 78.4680 | 94.1020 | BaiduDrive(j2ox) | se_resnet101.log |
SK-ResNet101 | 45.68M | 7.978 | 78.7920 | 94.2680 | BaiduDrive(boii) | sk_resnet101.log |
BAM-ResNet101 | 44.91M | 7.933 | 78.2180 | 94.0180 | ||
CBAM-ResNet101 | 49.33M | 7.879 | 78.3540 | 94.0640 | BaiduDrive(syj3) | cbam_resnet101.log |
GC-ResNet101 | ||||||
SGE-ResNet101 | 44.55M | 7.858 | 78.7980 | 94.3680 | BaiduDrive(wqn6) | sge_resnet101.log |
If you use related works in your research, please cite the paper:
@inproceedings{li2019selective,
title={Selective Kernel Networks},
author={Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Yang, Jian},
journal={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}
@inproceedings{li2019spatial,
title={Spatial Group-wise Enhance: Enhancing Semantic Feature Learning in Convolutional Networks},
author={Li, Xiang and Hu, Xiaolin and Yang, Jian},
journal={Arxiv},
year={2019}
}