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.mat
Trained 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
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 some attributes may not have a positive (or negative) sample, so F1 score of these attributes will be zero.
+------------+----------+-----------+--------+----------+
| attribute | accuracy | precision | recall | f1 score |
+------------+----------+-----------+--------+----------+
| young | 0.998 | 0.000 | 0.000 | 0.000 |
| teenager | 0.855 | 0.907 | 0.930 | 0.918 |
| adult | 0.880 | 0.502 | 0.240 | 0.325 |
| old | 0.945 | 0.000 | 0.000 | 0.000 |
| backpack | 0.760 | 0.550 | 0.229 | 0.323 |
| bag | 0.739 | 0.321 | 0.066 | 0.110 |
| handbag | 0.902 | 0.147 | 0.008 | 0.015 |
| clothes | 0.867 | 0.916 | 0.935 | 0.925 |
| down | 0.865 | 0.917 | 0.879 | 0.897 |
| up | 0.935 | 0.935 | 1.000 | 0.966 |
| hair | 0.791 | 0.777 | 0.592 | 0.672 |
| hat | 0.971 | 1.000 | 0.003 | 0.005 |
| gender | 0.780 | 0.764 | 0.725 | 0.744 |
| upblack | 0.904 | 0.663 | 0.574 | 0.615 |
| upwhite | 0.869 | 0.732 | 0.797 | 0.764 |
| upred | 0.941 | 0.704 | 0.742 | 0.723 |
| uppurple | 0.974 | 0.557 | 0.353 | 0.432 |
| upyellow | 0.968 | 0.904 | 0.723 | 0.803 |
| upgray | 0.879 | 0.621 | 0.242 | 0.349 |
| upblue | 0.924 | 0.705 | 0.152 | 0.250 |
| upgreen | 0.952 | 0.726 | 0.545 | 0.623 |
| downblack | 0.834 | 0.756 | 0.841 | 0.797 |
| downwhite | 0.943 | 0.474 | 0.484 | 0.479 |
| downpink | 0.981 | 0.682 | 0.552 | 0.610 |
| downpurple | 0.992 | 0.000 | 0.000 | 0.000 |
| downyellow | 0.995 | 0.000 | 0.000 | 0.000 |
| downgray | 0.848 | 0.689 | 0.238 | 0.354 |
| downblue | 0.822 | 0.685 | 0.206 | 0.316 |
| downgreen | 0.973 | 0.600 | 0.042 | 0.079 |
| downbrown | 0.930 | 0.490 | 0.309 | 0.379 |
+------------+----------+-----------+--------+----------+
Average accuracy: 0.9006
Average f1 score: 0.4491
+-----------+----------+-----------+--------+----------+
| attribute | accuracy | precision | recall | f1 score |
+-----------+----------+-----------+--------+----------+
| backpack | 0.677 | 0.660 | 0.842 | 0.740 |
| bag | 0.832 | 0.292 | 0.019 | 0.035 |
| handbag | 0.898 | 0.043 | 0.028 | 0.034 |
| boots | 0.785 | 0.521 | 0.429 | 0.471 |
| gender | 0.694 | 0.599 | 0.609 | 0.604 |
| hat | 0.794 | 0.755 | 0.248 | 0.373 |
| shoes | 0.886 | 0.531 | 0.128 | 0.206 |
| top | 0.876 | 0.466 | 0.108 | 0.175 |
| upblack | 0.766 | 0.786 | 0.861 | 0.821 |
| upwhite | 0.941 | 0.543 | 0.279 | 0.368 |
| upred | 0.961 | 0.598 | 0.459 | 0.520 |
| uppurple | 0.996 | 0.000 | 0.000 | 0.000 |
| upgray | 0.852 | 0.270 | 0.173 | 0.211 |
| upblue | 0.920 | 0.610 | 0.291 | 0.394 |
| upgreen | 0.977 | 0.631 | 0.098 | 0.170 |
| upbrown | 0.980 | 0.333 | 0.003 | 0.006 |
| downblack | 0.749 | 0.693 | 0.785 | 0.736 |
| downwhite | 0.925 | 0.533 | 0.145 | 0.228 |
| downred | 0.984 | 0.454 | 0.234 | 0.309 |
| downgray | 0.926 | 0.338 | 0.039 | 0.070 |
| downblue | 0.763 | 0.752 | 0.461 | 0.572 |
| downgreen | 0.997 | 0.000 | 0.000 | 0.000 |
| downbrown | 0.963 | 0.652 | 0.263 | 0.375 |
+-----------+----------+-----------+--------+----------+
Average accuracy: 0.8758
Average f1 score: 0.3226
>> 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: no
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: blue
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.
[1] Lin Y, Zheng L, Zheng Z, et al. Improving person re-identification by attribute and identity learning[J]. Pattern Recognition, 2019.
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