PulseFocusPlatform/docs/feature_models/SSLD_PRETRAINED_MODEL_en.md

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Simple semi-supervised label knowledge distillation solution (SSLD)

R-CNN on COCO

Backbone	Model	Images/GPU	Lr schd	FPS	Box AP	Mask AP	Download	Config
ResNet50-vd-SSLDv2-FPN	Faster	1	1x	----	41.4	-	model	config
ResNet50-vd-SSLDv2-FPN	Faster	1	2x	----	42.3	-	model	config
ResNet50-vd-SSLDv2-FPN	Mask	1	1x	----	42.0	38.2	model	config
ResNet50-vd-SSLDv2-FPN	Mask	1	2x	----	42.7	38.9	model	config
ResNet50-vd-SSLDv2-FPN	Cascade Faster	1	1x	----	44.4	-	model	config
ResNet50-vd-SSLDv2-FPN	Cascade Faster	1	2x	----	45.0	-	model	config
ResNet50-vd-SSLDv2-FPN	Cascade Mask	1	1x	----	44.9	39.1	model	config
ResNet50-vd-SSLDv2-FPN	Cascade Mask	1	2x	----	45.7	39.7	model	config

YOLOv3 on COCO

Backbone	Input shape	Images/GPU	Lr schd	FPS	Box AP	Download	Config
MobileNet-V1-SSLD	608	8	270e	----	31.0	model	config
MobileNet-V1-SSLD	416	8	270e	----	30.6	model	config
MobileNet-V1-SSLD	320	8	270e	----	28.4	model	config

YOLOv3 on Pasacl VOC

Backbone	Input shape	Images/GPU	Lr schd	FPS	Box AP	Download	Config
MobileNet-V1-SSLD	608	8	270e	-	78.3	model	config
MobileNet-V1-SSLD	416	8	270e	-	79.6	model	config
MobileNet-V1-SSLD	320	8	270e	-	77.3	model	config
MobileNet-V3-SSLD	608	8	270e	-	80.4	model	config
MobileNet-V3-SSLD	416	8	270e	-	79.2	model	config
MobileNet-V3-SSLD	320	8	270e	-	77.3	model	config

Notes:

• SSLD is a knowledge distillation method. We use the stronger backbone pretrained model after distillation to further improve the detection accuracy. Please refer to the knowledge distillation tutorial.

Citations

@misc{cui2021selfsupervision,
      title={Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones},
      author={Cheng Cui and Ruoyu Guo and Yuning Du and Dongliang He and Fu Li and Zewu Wu and Qiwen Liu and Shilei Wen and Jizhou Huang and Xiaoguang Hu and Dianhai Yu and Errui Ding and Yanjun Ma},
      year={2021},
      eprint={2103.05959},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}