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A simple baseline implemented in PyTorch for pedestrian attribute recognition task, evaluating on Market-1501 and DukeMTMC-reID dataset.
| Date | Stars |
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| 2026-07-24 | 404 |
| 2026-07-25 | 404 |
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| 2026-08-06 | 404 |
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# Person-Attribute-Recognition-MarketDuke A simple baseline implemented in PyTorch for **pedestrian attribute recognition** task, evaluating on Market-1501-attribute and DukeMTMC-reID-attribute dataset. ## Dataset You can get [Market-1501-attribute](https://github.com/vana77/Market-1501_Attribute) and [DukeMTMC-reID-attribute](https://github.com/vana77/DukeMTMC-attribute) annotations from [here](https://github.com/vana77). Also you need to download Market-1501 and DukeMTMC-reID dataset. Then, create a folder named 'attribute' under your dataset path, and put corresponding annotations into the folder. For example,<br> ``` ├── dataset │ ├── DukeMTMC-reID │ ├── bounding_box_test │ ├── bounding_box_train │ ├── query │ ├── attribute │ ├── duke_attribute.mat ``` ## Model Trained model are provided. You may download it from [Google Drive](https://drive.google.com/drive/folders/1JTdjuEbxSLypnfUzVuuxLj1uSKAacfd0?usp=sharing) or [Baidu Drive](https://pan.baidu.com/s/1bByCxZp9bSs8YYZPbuK21A) (提取码：jpks). You may download it and move `checkpoints` folder to your project's root directory. ## Dependencies * Python 3.5 * PyTorch >= 0.4.1 * torchvision >= 0.2.1 * matplotlib, sklearn, prettytable (optional) ## Usage ``` python3 train.py --data-path ~/dataset --dataset [market | duke] --model resnet50 [--use-id] python3 test.py --data-path ~/dataset --dataset [market | duke] --model resnet50 [--print-table] python3 inference.py test_sample/test_market.jpg [--dataset market] [--model resnet50] ``` ## Result We use **binary classification** settings (considered each attribute as an independent binary classification problem), and the classification threshold is **0.5**. ***Note that the precision, recall and f1 score are denoted as '-' for some ill-defined cases.*** ### Market-1501 gallery ``` +------------+----------+-----------+--------+----------+ | attribute | accuracy | precision | recall | f1 score | +------------+----------+-----------+--------+----------+ | young | 0.998 | 0.533 | 0.267 | 0.356 | | teenager | 0.892 | 0.927 | 0.951 | 0.939 | | adult | 0.895 | 0.582 | 0.450 | 0.508 | | old | 0.992 | 0.037 | 0.012 | 0.019 | | backpack | 0.883 | 0.828 | 0.672 | 0.742 | | bag | 0.790 | 0.608 | 0.378 | 0.467 | | handbag | 0.893 | 0.254 | 0.065 | 0.104 | | clothes | 0.946 | 0.956 | 0.984 | 0.970 | | down | 0.945 | 0.968 | 0.949 | 0.959 | | up | 0.936 | 0.938 | 0.998 | 0.967 | | hair | 0.877 | 0.871 | 0.773 | 0.819 | | hat | 0.982 | 0.812 | 0.505 | 0.623 | | gender | 0.919 | 0.947 | 0.864 | 0.903 | | upblack | 0.954 | 0.859 | 0.790 | 0.823 | | upwhite | 0.926 | 0.846 | 0.882 | 0.863 | | upred | 0.974 | 0.904 | 0.840 | 0.871 | | uppurple | 0.985 | 0.703 | 0.815 | 0.755 | | upyellow | 0.976 | 0.895 | 0.836 | 0.865 | | upgray | 0.909 | 0.852 | 0.391 | 0.537 | | upblue | 0.946 | 0.868 | 0.420 | 0.566 | | upgreen | 0.966 | 0.790 | 0.713 | 0.750 | | downblack | 0.879 | 0.815 | 0.889 | 0.850 | | downwhite | 0.956 | 0.608 | 0.550 | 0.578 | | downpink | 0.989 | 0.795 | 0.782 | 0.788 | | downpurple | 1.000 | - | - | - | | downyellow | 0.999 | 0.000 | 0.000 | 0.000 | | downgray | 0.878 | 0.756 | 0.443 | 0.559 | | downblue | 0.861 | 0.762 | 0.446 | 0.563 | | downgreen | 0.978 | 0.766 | 0.295 | 0.426 | | downbrown | 0.958 | 0.754 | 0.590 | 0.662 | +------------+----------+-----------+--------+----------+ Average accuracy: 0.9361 Average f1 score: 0.6492 ``` ### DukeMTMC-ReID gallery ``` +-----------+----------+-----------+--------+----------+ | attribut
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