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Release PaddleSeg release/2.9.2, adding new models and supporting all…
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| ## 目录 | ||
| - [一站式全流程开发简介](#1) | ||
| - [图像分割相关能力支持](#2) | ||
| - [图像分割相关模型产线列表和教程](#3) | ||
| - [图像分割相关单功能模块列表和教程](#4) | ||
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| <a name="1"></a> | ||
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| ## 1. 一站式全流程开发简介 | ||
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| 飞桨一站式全流程开发工具[PaddleX](https://github.com/PaddlePaddle/PaddleX/tree/release/3.0-beta1)，依托于PaddleSeg的先进技术，支持了图像分割领域的**一站式全流程**开发能力。通过一站式全流程开发，可实现简单且高效的模型使用、组合与定制。这将显著**减少模型开发的时间消耗**，**降低其开发难度**，大大加快模型在行业中的应用和推广速度。特色如下： | ||
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| * 🎨 **模型丰富一键调用**：将通用语义分割和图像异常检测涉及的**19个模型**整合为2条模型产线，通过极简的**Python API一键调用**，快速体验模型效果。此外，同一套API，也支持图像分类、目标检测、文本图像智能分析、通用OCR、时序预测等共计**200+模型**，形成20+单功能模块，方便开发者进行**模型组合使用**。 | ||
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| * 🚀 **提高效率降低门槛**：提供基于**统一命令**和**图形界面**两种方式，实现模型简洁高效的使用、组合与定制。支持**高性能部署、服务化部署和端侧部署**等多种部署方式。此外，对于各种主流硬件如**英伟达GPU、昆仑芯、昇腾、寒武纪和海光**等，进行模型开发时，都可以**无缝切换**。 | ||
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| >**说明**：PaddleX 致力于实现产线级别的模型训练、推理与部署。模型产线是指一系列预定义好的、针对特定AI任务的开发流程，其中包含能够独立完成某类任务的单模型（单功能模块）组合。 | ||
| <a name="2"></a> | ||
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| ## 2. 图像分割相关能力支持 | ||
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| PaddleX中图像分割相关的2条产线均支持本地**快速推理**，部分产线支持**在线体验**，您可以快速体验各个产线的预训练模型效果，如果您对产线的预训练模型效果满意，可以直接对产线进行[高性能部署](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_deploy/high_performance_deploy.md)/[服务化部署](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_deploy/service_deploy.md)/[端侧部署](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_deploy/lite_deploy.md)，如果不满意，您也可以使用产线的**二次开发**能力，提升效果。完整的产线开发流程请参考[PaddleX产线使用概览](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/pipeline_develop_guide.md)或各产线使用教程。 | ||
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| 此外，PaddleX为开发者提供了基于[云端图形化开发界面](https://aistudio.baidu.com/pipeline/mine)的全流程开发工具, 详细请参考[教程《零门槛开发产业级AI模型》](https://aistudio.baidu.com/practical/introduce/546656605663301) | ||
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| <table > | ||
| <tr> | ||
| <td></td> | ||
| <td>在线体验</td> | ||
| <td>快速推理</td> | ||
| <td>高性能部署</td> | ||
| <td>服务化部署</td> | ||
| <td>端侧部署</td> | ||
| <td>二次开发</td> | ||
| <td><a href = "https://aistudio.baidu.com/pipeline/mine">星河零代码产线</a></td> | ||
| </tr> | ||
| <tr> | ||
| <td>通用图像分类</td> | ||
| <td><a href = "https://aistudio.baidu.com/community/app/100061/webUI?source=appMineRecent">链接</a></td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| </tr> | ||
| <tr> | ||
| <td>图像异常检测</td> | ||
| <td>🚧</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>🚧</td> | ||
| <td>✅</td> | ||
| <td>🚧</td> | ||
| </tr> | ||
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| </table> | ||
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| > ❗注：以上功能均基于GPU/CPU实现。PaddleX还可在昆仑、昇腾、寒武纪和海光等主流硬件上进行快速推理和二次开发。下表详细列出了模型产线的支持情况，具体支持的模型列表请参阅 [模型列表(NPU)](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_npu.md) // [模型列表(XPU)](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_xpu.md) // [模型列表(MLU)](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_mlu.md) // [模型列表DCU](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/support_list/model_list_dcu.md)。同时我们也在适配更多的模型，并在主流硬件上推动高性能和服务化部署的实施。 | ||
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| **🚀 国产化硬件能力支持** | ||
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| <table> | ||
| <tr> | ||
| <th>产线名称</th> | ||
| <th>昇腾 910B</th> | ||
| <th>昆仑 R200/R300</th> | ||
| <th>寒武纪 MLU370X8</th> | ||
| <th>海光 Z100</th> | ||
| </tr> | ||
| <tr> | ||
| <td>通用语义分割</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| <td>✅</td> | ||
| </tr> | ||
| </table> | ||
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| <a name="3"></a> | ||
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| ## 3. 图像分割相关模型产线列表和教程 | ||
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| - **通用语义分割产线**: [使用教程](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.md) | ||
| - **图像异常检测产线**: [使用教程](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.md) | ||
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| <a name="4"></a> | ||
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| ## 4. 图像分割相关单功能模块列表和教程 | ||
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| - **语义分割模块**: [使用教程](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/semantic_segmentation.md) | ||
| - **图像异常检测模块**: [使用教程](https://github.com/PaddlePaddle/PaddleX/blob/release/3.0-beta1/docs/module_usage/tutorials/cv_modules/anomaly_detection.md) |
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| # 快速开始 | ||
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| >**说明：** | ||
| >* 飞桨一站式全流程开发工具[PaddleX](https://github.com/PaddlePaddle/PaddleX/tree/release/3.0-beta1)，依托于PaddleSeg的先进技术，支持了图像分割领域的**一站式全流程**开发能力。通过一站式全流程开发，可实现简单且高效的模型使用、组合与定制。 | ||
| >* PaddleX 致力于实现产线级别的模型训练、推理与部署。模型产线是指一系列预定义好的、针对特定AI任务的开发流程，其中包含能够独立完成某类任务的单模型（单功能模块）组合。本文档提供**图像分割相关产线**的快速使用，单功能模块的快速使用以及更多功能请参考[PaddleSeg一站式全流程开发](./overview.md)中相关章节。 | ||
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| ### 🛠️ 安装 | ||
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| > ❗安装PaddleX前请先确保您有基础的**Python运行环境**。 | ||
| * **安装PaddlePaddle** | ||
| ```bash | ||
| # cpu | ||
| python -m pip install paddlepaddle | ||
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| # gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境 | ||
| python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ | ||
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| # gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境 | ||
| python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/ | ||
| ``` | ||
| > ❗ 更多飞桨 Wheel 版本请参考[飞桨官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)。 | ||
| * **安装PaddleX** | ||
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| ```bash | ||
| pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0.beta1-py3-none-any.whl | ||
| ``` | ||
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| > ❗ 更多安装方式参考[PaddleX安装教程](./installation/installation.md) | ||
| ### 💻 命令行使用 | ||
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| 一行命令即可快速体验产线效果，统一的命令行格式为： | ||
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| ```bash | ||
| paddlex --pipeline [产线名称] --input [输入图片] --device [运行设备] | ||
| ``` | ||
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| 只需指定三个参数： | ||
| * `pipeline`：产线名称 | ||
| * `input`：待处理的输入图片的本地路径或URL | ||
| * `device`: 使用的GPU序号（例如`gpu:0`表示使用第0块GPU），也可选择使用CPU（`cpu`） | ||
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| 以通用OCR产线为例： | ||
| ```bash | ||
| paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0 | ||
| ``` | ||
| <details> | ||
| <summary><b>👉 点击查看运行结果 </b></summary> | ||
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| ```bash | ||
| {'img_path': '/root/.paddlex/predict_input/general_ocr_002.png', 'dt_polys': [[[5, 12], [88, 10], [88, 29], [5, 31]], [[208, 14], [249, 14], [249, 22], [208, 22]], [[695, 15], [824, 15], [824, 60], [695, 60]], [[158, 27], [355, 23], [356, 70], [159, 73]], [[421, 25], [659, 19], [660, 59], [422, 64]], [[337, 104], [460, 102], [460, 127], [337, 129]], [[486, 103], [650, 100], [650, 125], [486, 128]], [[675, 98], [835, 94], [835, 119], [675, 124]], [[64, 114], [192, 110], [192, 131], [64, 134]], [[210, 108], [318, 106], [318, 128], [210, 130]], [[82, 140], [214, 138], [214, 163], [82, 165]], [[226, 136], [328, 136], [328, 161], [226, 161]], [[404, 134], [432, 134], [432, 161], [404, 161]], [[509, 131], [570, 131], [570, 158], [509, 158]], [[730, 138], [771, 138], [771, 154], [730, 154]], [[806, 136], [817, 136], [817, 146], [806, 146]], [[342, 175], [470, 173], [470, 197], [342, 199]], [[486, 173], [616, 171], [616, 196], [486, 198]], [[677, 169], [813, 166], [813, 191], [677, 194]], [[65, 181], [170, 177], [171, 202], [66, 205]], [[96, 208], [171, 205], [172, 230], [97, 232]], [[336, 220], [476, 215], [476, 237], [336, 242]], [[507, 217], [554, 217], [554, 236], [507, 236]], [[87, 229], [204, 227], [204, 251], [87, 254]], [[344, 240], [483, 236], [483, 258], [344, 262]], [[66, 252], [174, 249], [174, 271], [66, 273]], [[75, 279], [264, 272], [265, 297], [76, 303]], [[459, 297], [581, 295], [581, 320], [459, 322]], [[101, 314], [210, 311], [210, 337], [101, 339]], [[68, 344], [165, 340], [166, 365], [69, 368]], [[345, 350], [662, 346], [662, 368], [345, 371]], [[100, 459], [832, 444], [832, 465], [100, 480]]], 'dt_scores': [0.8183103704439653, 0.7609575621092027, 0.8662357274035412, 0.8619508290334809, 0.8495855993183273, 0.8676840017933314, 0.8807986687956436, 0.822308525056085, 0.8686617037621976, 0.8279022169854463, 0.952332847006758, 0.8742692553015098, 0.8477013022907575, 0.8528771493227294, 0.7622965906848765, 0.8492388224448705, 0.8344203789965632, 0.8078477124353284, 0.6300434587457232, 0.8359967356998494, 0.7618617265751318, 0.9481573079350023, 0.8712182945408912, 0.837416955846334, 0.8292475059403851, 0.7860382856406026, 0.7350527486717117, 0.8701022267947695, 0.87172526903969, 0.8779847108088126, 0.7020437651809734, 0.6611684983372949], 'rec_text': ['www.997', '151', 'PASS', '登机牌', 'BOARDING', '舱位 CLASS', '序号SERIALNO.', '座位号SEATNO', '航班 FLIGHT', '日期DATE', 'MU 2379', '03DEC', 'W', '035', 'F', '1', '始发地FROM', '登机口 GATE', '登机时间BDT', '目的地TO', '福州', 'TAIYUAN', 'G11', 'FUZHOU', '身份识别IDNO.', '姓名NAME', 'ZHANGQIWEI', '票号TKTNO.', '张祺伟', '票价FARE', 'ETKT7813699238489/1', '登机口于起飞前10分钟关闭GATESCLOSE1OMINUTESBEFOREDEPARTURETIME'], 'rec_score': [0.9617719054222107, 0.4199012815952301, 0.9652514457702637, 0.9978302121162415, 0.9853208661079407, 0.9445787072181702, 0.9714463949203491, 0.9841841459274292, 0.9564052224159241, 0.9959094524383545, 0.9386572241783142, 0.9825271368026733, 0.9356589317321777, 0.9985442161560059, 0.3965512812137604, 0.15236201882362366, 0.9976775050163269, 0.9547433257102966, 0.9974752068519592, 0.9646636843681335, 0.9907559156417847, 0.9895358681678772, 0.9374122023582458, 0.9909093379974365, 0.9796401262283325, 0.9899340271949768, 0.992210865020752, 0.9478569626808167, 0.9982215762138367, 0.9924325942993164, 0.9941263794898987, 0.96443772315979]} | ||
| ...... | ||
| ``` | ||
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| 可视化结果如下： | ||
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|  | ||
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| </details> | ||
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| 其他产线的命令行使用，只需将`pipeline`参数调整为相应产线的名称。下面列出了每个产线对应的命令： | ||
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| | 产线名称 | 使用命令 | | ||
| |-----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ||
| | 文档场景信息抽取 | | | ||
| | 通用图像分类 | `paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0` | | ||
| | 通用OCR | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0` | | ||
| | 通用表格识别 | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0` | | ||
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| ### 📝 Python脚本使用 | ||
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| 几行代码即可完成产线的快速推理，统一的Python脚本格式如下： | ||
| ```python | ||
| from paddlex import create_pipeline | ||
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| pipeline = create_pipeline(pipeline=[产线名称]) | ||
| output = pipeline.predict([输入图片名称]) | ||
| for batch in output: | ||
| for item in batch: | ||
| res = item['result'] | ||
| res.print() | ||
| res.save_to_img("./output/") | ||
| res.save_to_json("./output/") | ||
| ``` | ||
| 执行了如下几个步骤： | ||
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| * `create_pipeline()` 实例化产线对象 | ||
| * 传入图片并调用产线对象的`predict` 方法进行推理预测 | ||
| * 对预测结果进行处理 | ||
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| 其他产线的Python脚本使用，只需将`create_pipeline()`方法的`pipeline`参数调整为相应产线的名称。下面列出了每个产线对应的参数名称及详细的使用解释： | ||
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| | 产线名称 | 对应参数 | 详细说明 | | ||
| |----------|----------------------|------| | ||
| | 通用OCR产线 | `OCR` | [通用OCR产线Python脚本使用说明](./pipeline_usage/OCR.md#222-python脚本方式集成) | | ||
| | 通用表格识别产线 | `table_recognition` | [通用表格识别产线Python脚本使用说明](./pipeline_usage/table_recognition.md#22-python脚本方式集成) | | ||
| | PP-ChatOCRv3产线 | `pp_chatocrv3` | [PP-ChatOCRv3产线Python脚本使用说明](./pipeline_usage/document_scene_information_extraction.md#222-python脚本方式集成) | |