--- title: cv_resnet50_pedestrian-attribute-recognition_image canonical_url: "https://www.modelscope.cn/models/iic/cv_resnet50_pedestrian-attribute-recognition_image" md_url: "https://www.modelscope.cn/models/iic/cv_resnet50_pedestrian-attribute-recognition_image.md" repository: iic/cv_resnet50_pedestrian-attribute-recognition_image chinese_name: "ResNet50行人结构化属性识别模型" last_updated: 2025-06-11 pipeline_tag: pedestrian-attribute-recognition tasks: - pedestrian-attribute-recognition library_name: - pytorch frameworks: - pytorch downloads: 347221 stars: 33 --- # cv_resnet50_pedestrian-attribute-recognition_image > cv_resnet50_pedestrian-attribute-recognition_image - iic 在 ModelScope 开源的模型。检测图片中人体的属性，具体功能包括人体检测以及属性预估。属性包括：性别、年龄、朝向、帽子、眼镜、手提包、背包、肩挎包、上衣种类、上衣颜色、下衣种类、下衣颜色。 - **Repository**: iic/cv_resnet50_pedestrian-attribute-recognition_image - **Tasks**: pedestrian-attribute-recognition - **Downloads**: 347221 - **Stars**: 33 - **Last updated**: 2025-06-11 Source: https://www.modelscope.cn/models/iic/cv_resnet50_pedestrian-attribute-recognition_image --- # 行人属性检测模型 输入一张图像，先进行人检测，再对检测到的人体区域进行属性检识别，输出所有人体区域检测框和属性值。 ## 模型描述 该模型主要用于行人属性识别任务，从图像中检测出人体框坐标和属性。该任务使用ReseNet50模型在PA100K和RAP综合数据集上训练而来，实验细节请参考论文Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization", ICCV 2019, Seoul, [https://arxiv.org/abs/1910.04562]。 ## 使用方式和范围 使用方式： - 输入任意图像，返回图像中所有的人体框坐标和属性值。 目标场景: 1. 安防场景。 2. 无人商店。 ### 如何使用 在ModelScope框架上，提供输入图片，即可以通过简单的Pipeline调用来完成行人属性识别任务。 #### 推理代码范例 也可以参考示例代码tests/pipelines/test_pedestrian_attribute_recognition.py ```python from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks model_id = 'damo/cv_resnet50_pedestrian-attribute-recognition_image' pedestrian_attribute_recognition = pipeline(Tasks.pedestrian_attribute_recognition, model=model_id, trust_remote_code=True) output = pedestrian_attribute_recognition('https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/keypoints_detect/000000442836.jpg') # the output contains boxes and labels print(output) ``` ### 模型局限性以及可能的偏差 - 受限于数据的多样性和数据量，衣服的种类和颜色值可能偏差比较大。 ## 训练数据介绍 训练数据来自公开数据集PA100K[https://www.v7labs.com/open-datasets/pa-100k]和RAP[https://www.rapdataset.com]。 ## 数据评估及结果 ### 测评指标 模型在公开测试数据集上的评价指标如下： | 输入大小 | mAP | Accuracy | Precision | Recall | F1_Score | mean-result | | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | | 384x192x3 | **0.831774** | **0.883914** | **0.933503** | **0.932833** | **0.933168** | **0.903038** | | Attr | p_true/n_true | p_tol/n_tol | p_pred/n_pred | cur_mA | | ------------ | ------------ | ------------ | ------------ | ------------ | | 0: Female | **1455\3351** | **1547\3419** | **1523\3443** | **0.96032** | | 1: AgeOver60 | **0\4984** | **0\4984** | **0\4984** | **0.50000** | | 2: Age18-60 | **4924\ 28** | **4930\ 54** | **4950\ 34** | **0.75865** | | 3: AgeLess18 | **26\4925** | **54\4930** | **31\4953** | **0.74023** | | 4: Front | **1303\3481** | **1395\3589** | **1411\3573** | **0.95198** | | 5: Side | **1704\2928** | **1893\3091** | **1867\3117** | **0.92371** | | 6: Back | **1615\3198** | **1696\3288** | **1705\3279** | **0.96243** | | 7: Hat | **49\4893** | **85\4899** | **55\4929** | **0.78762** | | 8: Glasses | **277\4557** | **359\4625** | **345\4639** | **0.87844** | | 9: HandBag | **200\4570** | **317\4667** | **297\4687** | **0.80507** | |10: ShoulderBag| **213\4603** | **312\4672** | **282\4702** | **0.83396** | |11: Backpack | **71\4861** | **105\4879** | **89\4895** | **0.83625** | |12: ShortSleeve| **61\4876** | **94\4890** | **75\4909** | **0.82304** | |13: LongSleeve | **4876\ 61** | **4890\ 94** | **4909\ 75** | **0.82304** | |14: Trousers | **4745\ 170** | **4781\ 203** | **4778\ 206** | **0.91495** | |15: Shorts | **0\4977** | **7\4977** | **0\4984** | **0.50000** | |16: Skirt&Dress| **277\4532** | **362\4622** | **367\4617** | **0.87286** | |17: 上黑 | **1973\2654** | **2134\2850** | **2169\2815** | **0.92789** | |18: 上灰 | **370\4400** | **457\4527** | **497\4487** | **0.89079** | |19: 上蓝 | **438\4428** | **502\4482** | **492\4492** | **0.93023** | |20: 上绿 | **468\4200** | **619\4365** | **633\4351** | **0.85913** | |21: 上白 | **327\4518** | **380\4604** | **413\4571** | **0.92092** | |22: 上紫 | **73\4878** | **86\4898** | **93\4891** | **0.92238** | |23: 上红 | **401\4505** | **431\4553** | **449\4535** | **0.95993** | |24: 上棕 | **55\4871** | **86\4898** | **82\4902** | **0.81701** | |25: 上黄 | **78\4863** | **95\4889** | **104\4880** | **0.90787** | |26: 上粉 | **102\4821** | **132\4852** | **133\4851** | **0.88317** | |27: 上橙 | **45\4905** | **62\4922** | **62\4922** | **0.86118** | |28: 下黑 | **2830\1705** | **3093\1891** | **3016\1968** | **0.90830** | |29: 下灰 | **165\4688** | **237\4747** | **224\4760** | **0.84189** | |30: 下蓝 | **939\3807** | **1032\3952** | **1084\3900** | **0.93660** | |31: 下绿 | **184\4586** | **264\4720** | **318\4666** | **0.83429** | |32: 下白 | **14\4944** | **28\4956** | **26\4958** | **0.74879** | |33: 下紫 | **0\4984** | **0\4984** | **0\4984** | **0.50000** | |34: 下红 | **33\4936** | **35\4949** | **46\4938** | **0.97012** | |35: 下棕 | **5\4966** | **13\4971** | **10\4974** | **0.69180** | |36: 下黄 | **39\4929** | **42\4942** | **52\4932** | **0.96297** | |37: 下粉 | **1\4980** | **3\4981** | **2\4982** | **0.66657** | |38: 下橙 | **1\4978** | **4\4980** | **3\4981** | **0.62480** | ### 模型效果 ![手部检测结果](assets/pedestrian_attribute1.jpg) ![手部检测结果](assets/pedestrian_attribute7.jpg) ![手部检测结果](assets/pedestrian_attribute8.jpg) ### 引用 ```BibTeX @article{DBLP:journals/corr/abs-1910-04562, author = {Chufeng Tang and Lu Sheng and Zhaoxiang Zhang and Xiaolin Hu}, title = {Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization}, journal = {CoRR}, volume = {abs/1910.04562}, year = {2019}, url = {http://arxiv.org/abs/1910.04562}, eprinttype = {arXiv}, eprint = {1910.04562}, timestamp = {Tue, 02 Aug 2022 14:18:00 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-04562.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` #### Clone with HTTP ```bash git clone https://www.modelscope.cn/damo/cv_resnet50_pedestrian-attribute-recognition_image.git ```