- Train
CUDA_VISIBLE_DEVICES=0 python train_cifar100.py --b 128
| Name | Resolution | #Params | Top-1 Acc. | Top-5 Acc. | BaiduDrive(models) |
|---|---|---|---|---|---|
| ResNet50 | 32 | 23.71M | 77.26 | 93.63 | - |
| + CBAM | 32 | 26.24M | 80.56 | 95.34 | - |
| + SA | 32 | 23.71M | 79.92 | 95.00 | - |
| + ECA | 32 | 23.71M | 79.68 | 95.05 | - |
| + NAM | 32 | 23.71M | 80.62 | 95.28 | - |
| + CA | 32 | 25.57M | 80.17 | 94.94 | - |
| + EMA | 32 | 23.85M | 80.69 | 95.59 | ema |
| + SSA-32 | 32 | 25.82M | 80.82 | 95.02 | - |
| + SSA-16 | 32 | 28.30M | 80.83 | 95.03 | - |
| + SSA-2 | 32 | 104.24M | 80.84 | 95.35 | - |
| ResNet101 | 32 | 42.70M | 77.78 | 94.39 | - |
| + CA | 32 | 46.22M | 80.01 | 94.78 | - |
| + EMA | 32 | 42.96M | 80.86 | 95.75 | - |
| + SSA-16 | 32 | 51.37M | 80.97 | 94.81 | - |
Training on ImageNet-1k with MobileNetv2 for 400 epochs.
- Train
./distributed_train.sh 2 ./ILSVRC2012/ --model mobilenetv2_100 -b 256 --sched cosine --epochs 400 --decay-epochs 2.4 --decay-rate .97 --opt-eps .001 -j 16 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --amp --lr 0.4 --warmup-epochs 5
- Val
python validate.py ./ILSVRC2012/ --model mobilenetv2_100 --checkpoint model_best.pth.tar --use-ema
| Name | Resolution | #Params | MFLOPs | Top-1 Acc. | Top-5 Acc. | BaiduDrive(models) |
|---|---|---|---|---|---|---|
| MobileNetv2 | 224 | 3.50M | 300 | 72.3 | 91.02 | |
| + SE | 224 | 3.89M | 300 | 73.5 | - | - |
| + CBAM | 224 | 3.89M | 300 | 73.6 | - | - |
| + CA | 224 | 3.95M | 310 | 74.3 | - | - |
| + EMA | 224 | 3.55M | 306 | 74.32 | 91.82 | ema |
Training on ImageNet-1k with MobileNetv2 for 200 epochs.
- Train
python imagenet.py -a mobilenetv2 -d <path-to-ILSVRC2012-data> --epochs 200 --lr-decay cos --lr 0.05 --wd 4e-5 -c <path-to-save-checkpoints> --input-size 224
| Name | Resolution | #Params | MFLOPs | Top-1 Acc. | Top-5 Acc. | BaiduDrive(models) |
|---|---|---|---|---|---|---|
| MobileNetv2 | 224 | 3.504M | 300.79 | 72.192 | 90.534 | - |
| + EMA | 224 | - | 302 | 72.55 | 90.89 | ema |
Training on COCO 2017 with YOLOv5s for 300 epochs.
- Train
python train.py --data coco.yaml --cfg yolov5s_EMA.yaml --weights yolov5s.pt --batch-size 64 --device 0
- Val
python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65 --weights yolov5s.pt
| Name | Resolution | #Params | MFLOPs | [email protected] | [email protected]:.95 | BaiduDrive(models) |
|---|---|---|---|---|---|---|
| YOLOv5s | 640 | 7.23M | 16.5 | 56.0 | 37.2 | yolov5s(v6.0) |
| + CBAM | 640 | 7.27M | 16.6 | 57.1 | 37.7 | cbam |
| + SA | 640 | 7.23M | 16.5 | 56.8 | 37.4 | sa |
| + ECA | 640 | 7.23M | 16.5 | 57.1 | 37.6 | eca |
| + CA | 640 | 7.26M | 16.50 | 57.5 | 38.1 | ca |
| + EMA | 640 | 7.24M | 16.53 | 57.8 | 38.4 | ema |
| + SSA-32 | 640 | 7.27M | 0 | 58.7 | 38.4 | |
| + SSA-16 | 640 | 7.31M | 0 | 58.1 | 38.5 | |
| + SSA-2 | 640 | 8.55M | 0 | 58.3 | 38.8 | |
| YOLOv5l | 640 | 46.50M | 109.1 | 67.2 | 48.8 | yolov5l(v6.0) |
| + SSA-16 | 640 | 46.70M | 0 | 0 | 0 |
Training on VisDrone 2019 with YOLOv5x.
- Train
python train.py --data VisDrone.yaml --weights yolov5x.pt --cfg models/accModels/yolov5xP2CBAM.yaml --epochs 300 --batch-size 6 --img 640 --device 0
- Val
python val.py --data VisDrone.yaml --img 640 --weights best.pt
| Name | Resolution | #Params | MFLOPs | [email protected] | [email protected]:.95 | BaiduDrive(models) |
|---|---|---|---|---|---|---|
| YOLOv5x (v6.0) | 640 | 90.96M | 314.2 | 49.29 | 30.0 | - |
| + CBAM | 640 | 91.31M | 315.1 | 49.40 | 30.1 | - |
| + CA | 640 | 91.28M | 315.2 | 49.30 | 30.1 | - |
| + EMA | 640 | 91.18M | 315.0 | 49.70 | 30.4 | ema |
| + SSA-16 | 640 | 91.42M | 317.5 | 0 | 0 |
@INPROCEEDINGS{10096516,
author={Ouyang, Daliang and He, Su and Zhang, Guozhong and Luo, Mingzhu and Guo, Huaiyong and Zhan, Jian and Huang, Zhijie},
booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Efficient Multi-Scale Attention Module with Cross-Spatial Learning},
year={2023},
pages={1-5},
doi={10.1109/ICASSP49357.2023.10096516}}
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