SOLO: Segmenting Objects by Locations - PaddlePaddle version

This directory is a realization scheme of PaddlePaddle high-performance deep learning framework.

PaddeDetection adopts the idea of modularization and has built-in rich data set enhancement strategy, backbone, network module and loss function. After understanding the excellent work of SOLO, we also implemented the PaddlePaddle version. We hope you can love it.

Model Zoo

Detector	Backbone	Multi-scale training	Lr schd	Mask APval	V100 FP32(FPS)	GPU	Download	Configs
YOLACT++	R50-FPN	False	80w iter	34.1 (test-dev)	33.5	Xp	-	-
CenterMask	R50-FPN	True	2x	36.4	13.9	Xp	-	-
CenterMask	V2-99-FPN	True	3x	40.2	8.9	Xp	-	-
PolarMask	R50-FPN	True	2x	30.5	9.4	V100	-	-
BlendMask	R50-FPN	True	3x	37.8	13.5	V100	-	-
SOLOv2 (Paper)	R50-FPN	False	1x	34.8	18.5	V100	-	-
SOLOv2 (Paper)	X101-DCN-FPN	True	3x	42.4	5.9	V100	-	-
SOLOv2	R50-FPN	False	1x	35.5	21.9	V100	model	config
SOLOv2	R50-FPN	True	3x	38.0	21.9	V100	model	config
SOLOv2	R101vd-FPN	True	3x	42.7	12.1	V100	model	config

Notes:

• SOLOv2 is trained on COCO train2017 dataset and evaluated on val2017 results of mAP(IoU=0.5:0.95).

Enhanced model

Backbone	Input size	Lr schd	V100 FP32(FPS)	Mask APval	Download	Configs
Light-R50-VD-DCN-FPN	512	3x	38.6	39.0	model	config

Optimizing method of enhanced model:

• Better backbone network: ResNet50vd-DCN
• A better pre-training model for knowledge distillation
• Exponential Moving Average
• Synchronized Batch Normalization
• Multi-scale training
• More data augmentation methods
• DropBlock

Training & Evaluation & Inference

PaddleDetection provides scripts for training, evalution and inference with various features according to different configure.
View full usage documentation

# training on single-GPU
export CUDA_VISIBLE_DEVICES=0
python tools/train.py -c configs/solov2/solov2_r50_fpn_3x_coco.yml
# training on multi-GPU
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/solov2/solov2_r50_fpn_3x_coco.yml
# GPU evaluation
export CUDA_VISIBLE_DEVICES=0
python tools/eval.py -c configs/solov2/solov2_r50_fpn_3x_coco.yml -o weights=output/solov2_r50_fpn_3x_coco/model_final
# Inference
python tools/infer.py -c configs/solov2/solov2_r50_fpn_3x_coco.yml --infer_img=demo/000000570688.jpg -o weights=output/solov2_r50_fpn_3x_coco/model_final

Citations

@article{wang2020solov2,
  title={SOLOv2: Dynamic and Fast Instance Segmentation},
  author={Wang, Xinlong and Zhang, Rufeng and  Kong, Tao and Li, Lei and Shen, Chunhua},
  journal={Proc. Advances in Neural Information Processing Systems (NeurIPS)},
  year={2020}
}