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README.md

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Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection (ARSL)

ARSL-FCOS 模型库

模型 COCO监督数据比例 Semi mAPval
0.5:0.95		 Semi Epochs (Iters) 模型下载 配置文件
ARSL-FCOS 1% 22.8 240 (87120) download config
ARSL-FCOS 5% 33.1 240 (174240) download config
ARSL-FCOS 10% 36.9 240 (174240) download config
ARSL-FCOS 10% 38.5(LSJ) 240 (174240) download config
ARSL-FCOS full(100%) 45.1 240 (174240) download config

使用说明

仅训练时必须使用半监督检测的配置文件去训练，评估、预测、部署也可以按基础检测器的配置文件去执行。

训练

# 单卡训练 (不推荐，需按线性比例相应地调整学习率)
CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/semi_det/arsl/arsl_fcos_r50_fpn_coco_semi010.yml --eval

# 多卡训练
python -m paddle.distributed.launch --log_dir=arsl_fcos_r50_fpn_coco_semi010/ --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/semi_det/arsl/arsl_fcos_r50_fpn_coco_semi010.yml --eval

评估

CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/semi_det/arsl/arsl_fcos_r50_fpn_coco_semi010.yml -o weights=output/arsl_fcos_r50_fpn_coco_semi010/model_final.pdparams

预测

CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/semi_det/arsl/arsl_fcos_r50_fpn_coco_semi010.yml -o weights=output/arsl_fcos_r50_fpn_coco_semi010/model_final.pdparams --infer_img=demo/000000014439.jpg

引用