add maskformer docs

docs/module_usage/tutorials/cv_modules/semantic_segmentation.en.md

@@ -190,9 +190,23 @@ Semantic segmentation is a technique in computer vision that classifies each pix
 <td>330.132</td>
 <td>6.1M</td>
 </tr>
+<tr>
+<td>MaskFormer_small</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/MaskFormer_small_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_small_pretrained.pdparams">Trained Model</a></td>
+<td>49.70</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>MaskFormer_tiny</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/MaskFormer_tiny_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_tiny_pretrained.pdparams">Trained Model</a></td>
+<td>46.69</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
 </tbody>
 </table>
-<p><b>The accuracy metrics of the SeaFormer series models are measured on the <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
+<p><b>The accuracy metrics of the above models are measured on the <a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a> dataset. GPU inference time is based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.</b></p></details>
 
 ## III. Quick Integration
 > ❗ Before quick integration, please install the PaddleX wheel package. For detailed instructions, refer to the [PaddleX Local Installation Guide](../../../installation/installation.en.md)

docs/module_usage/tutorials/cv_modules/semantic_segmentation.md

@@ -192,9 +192,23 @@ comments: true
 <td>330.132</td>
 <td>6.1M</td>
 </tr>
+<tr>
+<td>MaskFormer_small</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/MaskFormer_small_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_small_pretrained.pdparams">Trained Model</a></td>
+<td>49.70</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
+<tr>
+<td>MaskFormer_tiny</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0rc0/MaskFormer_tiny_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/MaskFormer_tiny_pretrained.pdparams">Trained Model</a></td>
+<td>46.69</td>
+<td></td>
+<td></td>
+<td></td>
+</tr>
 </tbody>
 </table>
-<p><b>SeaFormer系列模型的精度指标测量自<a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a>数据集。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b></p></details>
+<p><b>以上模型的精度指标测量自<a href="https://groups.csail.mit.edu/vision/datasets/ADE20K/">ADE20k</a>数据集。GPU 推理耗时基于 NVIDIA Tesla T4 机器，精度类型为 FP32， CPU 推理速度基于 Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz，线程数为 8，精度类型为 FP32。</b></p></details>
 
 ## 三、快速集成
 > ❗ 在快速集成前，请先安装 PaddleX 的 wheel 包，详细请参考 [PaddleX本地安装教程](../../../installation/installation.md)