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All Projects → hyk1996 → Person Attribute Recognition Marketduke

hyk1996 / Person Attribute Recognition Marketduke

A simple baseline implemented in PyTorch for pedestrian attribute recognition task, evaluating on Market-1501 and DukeMTMC-reID dataset.

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python
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pytorchcomputer-visioncnn

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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 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

Model

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.

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

+-----------+----------+-----------+--------+----------+
| 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

Inference

>> 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: blue

Update

20-06-03: 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.

Reference

[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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