Please provide PPOCRv4 recognition and detection trained model English language download link. Only the inference model is being downloaded by default. I want to fine-tune these weights over my own dataset.
/ [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_train.tar...
#安装检测服务模块:hub install deploy/hubserving/ocr_det/#或,安装分类服务模块:hub install deploy/hubserving/ocr_cls/#或,安装识别服务模块:hub install deploy/hubserving/ocr_rec/#或,安装检测+识别串联服务模块:hub install deploy/hubserving/ocr_system/#或,安装表格识别服务模块:hub install deploy/hubse...
分别打开 PaddleOCR-release-2.6\deploy\hubserving\ocr_system的 params.py并将模型路径参数改成刚刚下载的三个模型路径: cfg.cls_model_dir = "./inference/ch_ppocr_mobile_v2.0_cls_infer/" cfg.rec_model_dir = "./inference/ch_PP-OCRv4_rec_infer/" cfg.det_model_dir = "./inference/ch_PP-OC...
docker exec -it -u root fdocr bash 2.2 安装(在docker内) ```bash ldconfigapt-getinstall libgl1 ``` 2.3 启动服务端(在docker内) ```bash fastdeployserver --model-repository=/ocr_serving/models ``` 参数: - `model-repository`(required): 整套模型streaming_pp_tts存放的路径. ...
rec_model_dir='./model' ) def get_result(image:str)->str: """获取预测结果""" result = ocr.ocr(image) text = "" for i in result[0]: text += i[1][0]+'\n' return text #设置预测图片路径,这里建议使用绝对路径 img="/home/nano/code/ocr/test/1.jpg" ...
ch_ppocr_mobile_v2.0_rec_infer.tar -C /PaddleOCR/inference/ RUN hub install deploy/hubserving/ocr_system/ RUN hub install deploy/hubserving/ocr_cls/ RUN hub install deploy/hubserving/ocr_det/ RUN hub install deploy/hubserving/ocr_rec/ EXPOSE 9000 CMD ["/bin/bash","-c","hub serving ...
使用python tools/eval.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_svtr_large.yml也是正常的, 用python tools/export_model.py -c configs/rec/PP-OCRv4/ch_PP-OCRv4_rec_svtr_large.yml 也是能成功导出模型的 用export_model.py导出的模型,使用python tools/infer/predict_rec.py预测宽度不太长的...
hub serving start --modules ocr_system ocr_cls ocr_det ocr_rec -p 9000 识别地址就是 http://127.0.0.1:9000/predict/ocr_system 测试没问题,到这里运行镜像就构建好了 最后将容器内无用文件删除,减小容器的体积 rm -rf /root/.cache/* \ && rm -rf /var/lib/apt/lists/* \ && rm -rf /app...