修改模型表格（中文未加下载链接） #17108
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| # 文档图像方向分类模块使用教程 | ||||||
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| ## 一、概述 | ||||||
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| > 推理耗时仅包含模型推理耗时，不包含前后处理耗时。 | ||||||
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| ### 📐📐 PP-LCNet_x1_0_doc_ori | ||||||
| **模型类型：** 推理模型/训练模型 | **模型存储大小：** 7 MB | ||||||
| **模型介绍：** | ||||||
| 基于PP-LCNet_x1_0的文档图像分类模型，含有四个类别，即0度，90度，180度，270度。主要用于将文档图像的方向区分出来，并使用后处理将其矫正，提高OCR处理的准确性。 | ||||||
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| **性能指标：** | ||||||
| | 指标名称 | Top-1 Acc(%) | GPU推理耗时 (ms) | CPU推理耗时 (ms) | | ||||||
| | :--- | :--- | :--- | :--- | | ||||||
| | **常规模式** | 99.06 | 2.62 | 3.24 | | ||||||
| | **高性能模式** | - | 0.59 | 1.19 | | ||||||
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| **下载链接：** | ||||||
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Collaborator
There was a problem hiding this comment. 这部分建议改成表格结构，其他的也都修改成如下格式：
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| [推理模型](https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-LCNet_x1_0_doc_ori_infer.tar) | [训练模型](https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-LCNet_x1_0_doc_ori_pretrained.pdparams) | ||||||
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| [Hugging Face](https://huggingface.co/PaddlePaddle/PP-LCNet_x1_0_doc_ori) | ||||||
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| [ModelScope](https://www.modelscope.cn/models/PaddlePaddle/PP-LCNet_x1_0_doc_ori) | ||||||
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| [AI Studio](https://aistudio.baidu.com/modelsdetail/31905) | ||||||
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| --- | ||||||
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| ### 🧪🧪🧪 测试环境说明 | ||||||
| **性能测试环境：** | ||||||
| - **测试数据集：** 自建多场景数据集（1000张图片，含证件/文档等场景） | ||||||
| - **硬件配置：** | ||||||
| - GPU：NVIDIA Tesla T4 | ||||||
| - CPU：Intel Xeon Gold 6271C @ 2.60GHz | ||||||
| - **软件环境：** | ||||||
| - Ubuntu 20.04 / CUDA 11.8 / cuDNN 8.9 / TensorRT 8.6.1.6 | ||||||
| - paddlepaddle 3.0.0 / paddleocr 3.0.3 | ||||||
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| **推理模式说明：** | ||||||
| | 模式 | GPU配置 | CPU配置 | 加速技术组合 | | ||||||
| | :--- | :--- | :--- | :--- | | ||||||
| | **常规模式** | FP32精度 / 无TRT加速 | FP32精度 / 8线程 | PaddleInference | | ||||||
| | **高性能模式** | 选择先验精度类型和加速策略的最优组合 | FP32精度 / 8线程 | 选择先验最优后端（Paddle/OpenVINO/TRT等） | | ||||||
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| ## 三、快速开始 | ||||||
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模型名称前的小图标建议去掉，对于一部分模型图标能代表模型，但是很多模型无法使用合适的图标，所以统一去除