PytorchInsight

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.

Including Papers (to be updated):

Attention Models

• 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)
• SRMNet: SRM: A Style-based Recalibration Module for Convolutional Neural Networks _(paper)

Non-Attention Models

• OctNet: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution _(paper)
• imagenet_tricks.py: Bag of Tricks for Image Classification with Convolutional Neural Networks _(paper)

Trained Models and Performance Table

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

Classification

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)
SRM-ResNet50
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	BaiduDrive(z0h3)	bam_resnet50.log
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)
SRM-ResNet101
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	BaiduDrive(4bw6)	bam_resnet101.log
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

Detection

Model	#p	GFLOPs	Detector	Neck	${\rm AP}_{50:95}$ (%)	${\rm AP}_{50}$ (%)	${\rm AP}_{75}$ (%)	Download
ResNet50	23.51M	88.032	Faster RCNN	FPN	37.5	59.1	40.6	BaiduDrive()
SGE-ResNet50	23.51M	88.149	Faster RCNN	FPN	38.7	60.8	41.7	BaiduDrive()
ResNet50	23.51M	88.032	Mask RCNN	FPN	38.6	60.0	41.9	BaiduDrive()
SGE-ResNet50	23.51M	88.149	Mask RCNN	FPN	39.6	61.5	42.9	BaiduDrive()
ResNet50	23.51M	88.032	Cascade RCNN	FPN	41.1	59.3	44.8	BaiduDrive()
SGE-ResNet50	23.51M	88.149	Cascade RCNN	FPN	42.6	61.4	46.2	BaiduDrive()
ResNet101	42.50M	167.908	Faster RCNN	FPN	39.4	60.7	43.0	BaiduDrive()
SGE-ResNet101	42.50M	168.099	Faster RCNN	FPN	41.0	63.0	44.3	BaiduDrive()
ResNet101	42.50M	167.908	Mask RCNN	FPN	40.4	61.6	44.2	BaiduDrive()
SGE-ResNet101	42.50M	168.099	Mask RCNN	FPN	42.1	63.7	46.1	BaiduDrive()
ResNet101	42.50M	167.908	Cascade RCNN	FPN	42.6	60.9	46.4	BaiduDrive()
SGE-ResNet101	42.50M	168.099	Cascade RCNN	FPN	44.4	63.2	48.4	BaiduDrive()

Model	#p	GFLOPs	Detector	Neck	${\rm AP}_{\rm small}$ (%)	${\rm AP}_{\rm media}$ (%)	${\rm AP}_{\rm large}$ (%)	Download
ResNet50	23.51M	88.032	RetinaNet	FPN	19.9	39.6	48.3	BaiduDrive()
SE-ResNet50	26.04M	88.152	RetinaNet	FPN	20.7	41.3	50.0	BaiduDrive()
SK-ResNet50	24.11M	89.414	RetinaNet	FPN	20.2	40.9	50.4	BaiduDrive()
BAM-ResNet50	23.87M	89.804	RetinaNet	FPN	19.6	40.1	49.9	BaiduDrive()
CBAM-ResNet50	26.04M	88.302	RetinaNet	FPN	21.8	40.8	49.5	BaiduDrive()
SGE-ResNet50	23.51M	88.149	RetinaNet	FPN	21.8	41.2	49.9	BaiduDrive()

Citation

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}
}