Results

Training on CIFAR-100 with ResNet for 200 epochs.

• 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	-
+ SE	32	26.2M	79.71	94.82	-
+ BAM	32	24.1M	80.03	94.97	-
+ TAM	32	23.7M	79.85	94.87	-
+ GAM	32	149.5M	81.33	95.46	-
+ EMA	32	23.85M	80.69	95.59	ema
+ SSA-32	32	25.6M	80.76	95.57	ssa
+ SSA-16	32	27.5M	81.32	95.55	ssa
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-32	32	46.3M	81.37	95.29	ssa
+ SSA-16	32	50.0M	81.41	95.59	ssa

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	mAP@.5	mAP@.5:.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
+ SSA-1	640	11.50M	0	58.8	39.1

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	mAP@.5	mAP@.5:.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-32	640	91.18M	315.8	49.80	30.7

References

• NAM
• MobileNetv2
• YOLOv5s
• YOLOv5x
• CoordAttention

@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}}