pytorchOCR基于pytorch的ocr算法库
已完成模型:DBnet
PSEnet
PANnet
SASTnet
CRNN
检测模型效果:训练只在ICDAR文本检测公开数据集上模型骨干网络precisionrecallHmeanDBResNet50_7*785.88%79.10%82.35%DBResNet50_3*386.51%80.59%83.44%DBMobileNetV382.89%75.83%79.20%SASTResNet50_7*785.72%78.38%81.89%SASTResNet50_3*386.67%76.74%81.40%PSEResNet50_7*784.10%80.01%82.01%PSEResNet50_3*382.56%78.91%80.69%PANResNet18_7*781.80%77.08%79.37%PANResNet18_3*383.78%75.15%79.23%模型压缩剪枝效果:这里使用mobilev3作为backbone,在icdar上测试结果,未压缩模型初始大小为2.4M.1.对backbone进行压缩
模型prunedmethodratiomodelsize(M)precisionrecallHmeanDBno02.484.04%75.34%79.46%DBbackbone0.51.983.74%73.18%78.10%DBbackbone0.61.5884.46%69.90%76.50%2.对整个模型进行压缩
模型prunedmethodratiomodelsize(M)precisionrecallHmeanDBno02.485.70%74.77%79.86%DBtotal0.61.4282.97%75.10%78.84%DBtotal0.651.1585.14%72.84%78.51%模型蒸馏:模型teacherstudentmodelsize(M)precisionrecallHmeanimprove(%)DBnomobilev32.485.70%74.77%79.86%-DBresnet50mobilev32.486.37%77.22%81.54%1.68DBnomobilev31.4282.97%75.10%78.84%-DBresnet50mobilev31.4285.88%76.16%80.73%1.89DBnomobilev31.1585.14%72.84%78.51%-DBresnet50mobilev31.1585.60%74.72%79.79%1.28项目