A simple baseline implemented in PyTorch for pedestrian attribute recognition task, evaluating on Market-1501-attribute and DukeMTMC-reID-attribute dataset.
You can get Market-1501-attribute and DukeMTMC-reID-attribute annotations from here. 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,
├── dataset
│ ├── DukeMTMC-reID
│ ├── bounding_box_test
│ ├── bounding_box_train
│ ├── query
│ ├── attribute
│ ├── duke_attribute.matTrained model are provided. You may download it from Google Drive or Baidu Drive (提取码：jpks).
You may download it and move checkpoints folder to your project's root directory.
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]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.
+------------+----------+-----------+--------+----------+
| 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+-----------+----------+-----------+--------+----------+
| attribute | accuracy | precision | recall | f1 score |
+-----------+----------+-----------+--------+----------+
| backpack | 0.829 | 0.794 | 0.926 | 0.855 |
| bag | 0.836 | 0.496 | 0.287 | 0.364 |
| handbag | 0.935 | 0.469 | 0.073 | 0.126 |
| boots | 0.905 | 0.784 | 0.791 | 0.787 |
| gender | 0.858 | 0.806 | 0.828 | 0.817 |
| hat | 0.898 | 0.883 | 0.680 | 0.768 |
| shoes | 0.916 | 0.756 | 0.414 | 0.535 |
| top | 0.893 | 0.590 | 0.381 | 0.463 |
| upblack | 0.821 | 0.827 | 0.903 | 0.864 |
| upwhite | 0.959 | 0.750 | 0.509 | 0.606 |
| upred | 0.973 | 0.745 | 0.649 | 0.694 |
| uppurple | 0.995 | 0.258 | 0.123 | 0.167 |
| upgray | 0.900 | 0.611 | 0.333 | 0.432 |
| upblue | 0.943 | 0.766 | 0.519 | 0.619 |
| upgreen | 0.975 | 0.463 | 0.403 | 0.431 |
| upbrown | 0.980 | 0.481 | 0.328 | 0.390 |
| downblack | 0.787 | 0.740 | 0.807 | 0.772 |
| downwhite | 0.945 | 0.771 | 0.395 | 0.522 |
| downred | 0.991 | 0.739 | 0.645 | 0.689 |
| downgray | 0.927 | 0.471 | 0.238 | 0.317 |
| downblue | 0.807 | 0.741 | 0.669 | 0.703 |
| downgreen | 0.997 | - | - | - |
| downbrown | 0.979 | 0.871 | 0.594 | 0.706 |
+-----------+----------+-----------+--------+----------+
Average accuracy: 0.9152
Average f1 score: 0.5739>> python inference.py test_sample/test_market.jpg --dataset market
age: teenager
carrying backpack: no
carrying bag: no
carrying handbag: no
type of lower-body clothing: dress
length of lower-body clothing: short
sleeve length: short sleeve
hair length: long hair
wearing hat: no
gender: female
color of upper-body clothing: white
color of lower-body clothing: white
>> python inference.py test_sample/test_duke.jpg --dataset duke
carrying backpack: no
carrying bag: yes
carrying handbag: no
wearing boots: no
gender: male
wearing hat: no
color of shoes: dark
length of upper-body clothing: short upper body clothing
color of upper-body clothing: black
color of lower-body clothing: blue20-06-03: Added identity loss for joint optimization; Adjusted the learning rate for better performace.
20-06-03: Updated test.py, settled the issue of ill-defined metrics.
19-09-16: Updated inference.py, fixed the error caused by missing data-transform.
19-09-06: Updated test.py, added F1 score for evaluating.
19-09-03: Added inference.py, thanks @ViswanathaReddyGajjala.
19-08-23: Released trained models.
19-01-09: Fixed the error caused by an update of market and duke attribute dataset.
The label order in import_Market1501Attribute.py is consistent with the attribute order of the dataset.
You can load market_attribute.mat in MATLAB and print "market_attribute.train" or "market_attribute.test" to obtain these orders.
This repo consider attribute prediction as multiple binary classification, but some attribute have more than two categories.
For example, attribute 'age' in Market-1501 has four categories: young(1), teenager(2), adult(3), old(4). So it can be split into four attributes: 'young', 'teenager', 'adult' and 'old'.
That's why preds of Market-1501 has 30 attributes.
[1] Lin Y, Zheng L, Zheng Z, et al. Improving person re-identification by attribute and identity learning[J]. Pattern Recognition, 2019.