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AmberC0209 authored Nov 21, 2024
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1 change: 1 addition & 0 deletions .github/workflows/deploy_docs.yml
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push:
branches: #设置更新哪个分支会更新站点
- develop
- release/3.0-beta2
permissions:
contents: write
jobs:
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| [**PP-ChatOCRv3-doc**](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html) | [**Time Series Forecasting**](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html) | [**Time Series Anomaly Detection**](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html) | [**Time Series Classification**](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html) |
| <img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/e3d97f4e-ab46-411c-8155-494c61492b0a" height="126px" width="180px"> | <img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/6e897bf6-35fe-45e6-a040-e9a1a20cfdf2" height="126px" width="180px"> | <img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/c54c66cc-da4f-4631-877b-43b0fbb192a6" height="126px" width="180px"> | <img src="https://github.com/PaddlePaddle/PaddleX/assets/142379845/0ce925b2-3776-4dde-8ce0-5156d5a2476e" height="126px" width="180px"> |



## 🌟 Why PaddleX ?

🎨 **Rich Models One-click Call**: Integrate over **200 PaddlePaddle models** covering multiple key areas such as OCR, object detection, and time series forecasting into **19 pipelines**. Experience the model effects quickly through easy Python API calls. Also supports **more than 20 modules** for easy model combination use by developers.
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</table>



## 🌟 Why PaddleX ?

🎨 <b>Rich Models One-click Call</b>: Integrate over <b>200 PaddlePaddle models</b> covering multiple key areas such as OCR, object detection, and time series forecasting into <b>19 pipelines</b>. Experience the model effects quickly through easy Python API calls. Also supports <b>more than 20 modules</b> for easy model combination use by developers.
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<b>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。</b>


## 📊 能力支持
## 📊 能力支持

PaddleX的各个产线均支持本地<b>快速推理</b>，部分模型支持在[AI Studio星河社区](https://aistudio.baidu.com/overview)上进行<b>在线体验</b>，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[高性能推理](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/high_performance_inference.html)/[服务化部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/service_deploy.html)/[端侧部署](https://paddlepaddle.github.io/PaddleX/latest/pipeline_deploy/edge_deploy.html)，如果不满意，您也可以使用产线的<b>二次开发</b>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://paddlepaddle.github.io/PaddleX/latest/pipeline_usage/pipeline_develop_guide.html)或各产线使用[教程](#-文档)。

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</table>


> ❗注：以上功能均基于 GPU/CPU 实现。PaddleX 还可在昆仑芯、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(昆仑芯XPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_xpu.html)/[模型列表(昇腾NPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_npu.html)/[模型列表(寒武纪MLU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_mlu.html)/[模型列表(海光DCU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_dcu.html)。我们正在适配更多的模型，并在主流硬件上推动高性能和服务化部署的实施。
!!! tip
以上功能均基于 GPU/CPU 实现。PaddleX 还可在昆仑芯、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(昆仑芯XPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_xpu.html)/[模型列表(昇腾NPU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_npu.html)/[模型列表(寒武纪MLU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_mlu.html)/[模型列表(海光DCU)](https://paddlepaddle.github.io/PaddleX/latest/support_list/model_list_dcu.html)。我们正在适配更多的模型，并在主流硬件上推动高性能和服务化部署的实施。

🔥🔥 <b>国产化硬件能力支持</b>

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<table>
<tr>
<th>模型</th><th>Model Download Link</th>
<th>模型</th><th>模型下载链接</th>
<th>Mask AP</th>
<th>GPU推理耗时（ms）</th>
<th>CPU推理耗时 (ms)</th>
<th>模型存储大小（M）</th>
<th>介绍</th>
</tr>
<tr>
<td>Mask-RT-DETR-H</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-H_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-H_pretrained.pdparams">Trained Model</a></td>
<td>Mask-RT-DETR-H</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-H_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-H_pretrained.pdparams">训练模型</a></td>
<td>50.6</td>
<td>132.693</td>
<td>4896.17</td>
<td>449.9 M</td>
<td rowspan="5">Mask-RT-DETR 是一种基于RT-DETR的实例分割模型，通过采用最优性能的更好的PP-HGNetV2作为骨干网络，构建了MaskHybridEncoder编码器，引入了IOU-aware Query Selection 技术，使其在相同推理耗时上取得了SOTA实例分割精度。</td>
</tr>
<tr>
<td>Mask-RT-DETR-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-L_pretrained.pdparams">Trained Model</a></td>
<td>Mask-RT-DETR-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-L_pretrained.pdparams">训练模型</a></td>
<td>45.7</td>
<td>46.5059</td>
<td>2575.92</td>
Expand All @@ -41,118 +41,118 @@ comments: true

<table>
<tr>
<th>模型</th><th>Model Download Link</th>
<th>模型</th><th>模型下载链接</th>
<th>Mask AP</th>
<th>GPU推理耗时（ms）</th>
<th>CPU推理耗时 (ms)</th>
<th>模型存储大小（M）</th>
<th>介绍</th>
</tr>
<tr>
<td>Cascade-MaskRCNN-ResNet50-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Cascade-MaskRCNN-ResNet50-FPN_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Cascade-MaskRCNN-ResNet50-FPN_pretrained.pdparams">Trained Model</a></td>
<td>Cascade-MaskRCNN-ResNet50-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Cascade-MaskRCNN-ResNet50-FPN_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Cascade-MaskRCNN-ResNet50-FPN_pretrained.pdparams">训练模型</a></td>
<td>36.3</td>
<td>-</td>
<td>-</td>
<td>254.8 M</td>
<td rowspan="2">Cascade-MaskRCNN 是一种改进的Mask RCNN实例分割模型，通过级联多个检测器，利用不同IOU阈值优化分割结果，解决检测与推理阶段的mismatch问题，提高了实例分割的准确性。</td>
</tr>
<tr>
<td>Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN_pretrained.pdparams">Trained Model</a></td>
<td>Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Cascade-MaskRCNN-ResNet50-vd-SSLDv2-FPN_pretrained.pdparams">训练模型</a></td>
<td>39.1</td>
<td>-</td>
<td>-</td>
<td>254.7 M</td>
</tr>
<tr>
<td>Mask-RT-DETR-H</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-H_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-H_pretrained.pdparams">Trained Model</a></td>
<td>Mask-RT-DETR-H</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-H_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-H_pretrained.pdparams">训练模型</a></td>
<td>50.6</td>
<td>132.693</td>
<td>4896.17</td>
<td>449.9 M</td>
<td rowspan="5">Mask-RT-DETR 是一种基于RT-DETR的实例分割模型，通过采用最优性能的更好的PP-HGNetV2作为骨干网络，构建了MaskHybridEncoder编码器，引入了IOU-aware Query Selection 技术，使其在相同推理耗时上取得了SOTA实例分割精度。</td>
</tr>
<tr>
<td>Mask-RT-DETR-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-L_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-L_pretrained.pdparams">Trained Model</a></td>
<td>Mask-RT-DETR-L</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-L_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-L_pretrained.pdparams">训练模型</a></td>
<td>45.7</td>
<td>46.5059</td>
<td>2575.92</td>
<td>113.6 M</td>
</tr>
<tr>
<td>Mask-RT-DETR-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-M_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-M_pretrained.pdparams">Trained Model</a></td>
<td>Mask-RT-DETR-M</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-M_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-M_pretrained.pdparams">训练模型</a></td>
<td>42.7</td>
<td>36.8329</td>
<td>-</td>
<td>66.6 M</td>
</tr>
<tr>
<td>Mask-RT-DETR-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-S_pretrained.pdparams">Trained Model</a></td>
<td>Mask-RT-DETR-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-S_pretrained.pdparams">训练模型</a></td>
<td>41.0</td>
<td>33.5007</td>
<td>-</td>
<td>51.8 M</td>
</tr>
<tr>
<td>Mask-RT-DETR-X</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-X_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-X_pretrained.pdparams">Trained Model</a></td>
<td>Mask-RT-DETR-X</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/Mask-RT-DETR-X_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/Mask-RT-DETR-X_pretrained.pdparams">训练模型</a></td>
<td>47.5</td>
<td>75.755</td>
<td>3358.04</td>
<td>237.5 M</td>
</tr>
<tr>
<td>MaskRCNN-ResNet50-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet50-FPN_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet50-FPN_pretrained.pdparams">Trained Model</a></td>
<td>MaskRCNN-ResNet50-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet50-FPN_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet50-FPN_pretrained.pdparams">训练模型</a></td>
<td>35.6</td>
<td>-</td>
<td>-</td>
<td>157.5 M</td>
<td rowspan="6">Mask R-CNN是由华盛顿首例即现投影卡的一个全任务深度学习模型，能够在一个模型中完成图片实例的分类和定位，并结合图像级的遮罩（Mask）来完成分割任务。</td>
</tr>
<tr>
<td>MaskRCNN-ResNet50-vd-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet50-vd-FPN_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet50-vd-FPN_pretrained.pdparams">Trained Model</a></td>
<td>MaskRCNN-ResNet50-vd-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet50-vd-FPN_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet50-vd-FPN_pretrained.pdparams">训练模型</a></td>
<td>36.4</td>
<td>-</td>
<td>-</td>
<td>157.5 M</td>
</tr>
<tr>
<td>MaskRCNN-ResNet50</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet50_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet50_pretrained.pdparams">Trained Model</a></td>
<td>MaskRCNN-ResNet50</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet50_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet50_pretrained.pdparams">训练模型</a></td>
<td>32.8</td>
<td>-</td>
<td>-</td>
<td>128.7 M</td>
</tr>
<tr>
<td>MaskRCNN-ResNet101-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet101-FPN_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet101-FPN_pretrained.pdparams">Trained Model</a></td>
<td>MaskRCNN-ResNet101-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet101-FPN_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet101-FPN_pretrained.pdparams">训练模型</a></td>
<td>36.6</td>
<td>-</td>
<td>-</td>
<td>225.4 M</td>
</tr>
<tr>
<td>MaskRCNN-ResNet101-vd-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet101-vd-FPN_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet101-vd-FPN_pretrained.pdparams">Trained Model</a></td>
<td>MaskRCNN-ResNet101-vd-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNet101-vd-FPN_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNet101-vd-FPN_pretrained.pdparams">训练模型</a></td>
<td>38.1</td>
<td>-</td>
<td>-</td>
<td>225.1 M</td>
</tr>
<tr>
<td>MaskRCNN-ResNeXt101-vd-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNeXt101-vd-FPN_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNeXt101-vd-FPN_pretrained.pdparams">Trained Model</a></td>
<td>MaskRCNN-ResNeXt101-vd-FPN</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/MaskRCNN-ResNeXt101-vd-FPN_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskRCNN-ResNeXt101-vd-FPN_pretrained.pdparams">训练模型</a></td>
<td>39.5</td>
<td>-</td>
<td>-</td>
<td>370.0 M</td>
<td></td>
</tr>
<tr>
<td>PP-YOLOE_seg-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_seg-S_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_seg-S_pretrained.pdparams">Trained Model</a></td>
<td>PP-YOLOE_seg-S</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/PP-YOLOE_seg-S_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_seg-S_pretrained.pdparams">训练模型</a></td>
<td>32.5</td>
<td>-</td>
<td>-</td>
<td>31.5 M</td>
<td>PP-YOLOE_seg 是一种基于PP-YOLOE的实例分割模型。该模型沿用了PP-YOLOE的backbone和head，通过设计PP-YOLOE实例分割头，大幅提升了实例分割的性能和推理速度。</td>
</tr>
<tr>
<td>SOLOv2</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SOLOv2_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SOLOv2_pretrained.pdparams">Trained Model</a></td>
<td>SOLOv2</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0b2/SOLOv2_infer.tar">推理模型</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/SOLOv2_pretrained.pdparams">训练模型</a></td>
<td>35.5</td>
<td>-</td>
<td>-</td>
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</table>

> ❗ 以上列出的是目标检测模块重点支持的<b>6个核心模型</b>，该模块总共支持<b>37个模型</b>，完整的模型列表如下：
<details><summary> 👉模型列表详情</summary>

<table>
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