yuanxincherry/person-reid-evaluation

GOM：New Metric for Re-identification. 👉GOM explicitly balances the effect of performing retrieval and verification into a single unified metric.

Genuine Open-set re-ID Metric (GOM)

The official repository for Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric. Information Sciences (Under Review).

GOM evaluates the state-of-the-art methods on both supervised learning tasks and unsupervised learning tasks for object re-ID, including person re-ID and vehicle re-ID.

Illustration of existing retrieval and verification procedures. 1) Closed-world retrieval: the method returns a ranking list to users and cannot tell which images are the true targets. The user needs to judge targets according to their experience and feeling. 2) Closed-world verification: given a decision boundary, images whose distances are below the boundary are considered as the targets. Nevertheless, the method cannot distinguish ground truth (GT) and non-GT within the boundary. 3) Open-set: the GT of the probe does not always exist in the gallery, thus re-ID procedure should include this kind of scenario.

Comparison with existing metrics

A family of metrics of GOM

Prerequisites

• Python3.0+
• Scipy=1.1.0
• Numpy=1.15.1

Getting started

Check the Prerequisites. Then download GOM.py and test.py. Next put them in the same directory as you want.

Part 1: Prepare data

Extract distance, gallery_label, gallery_cam, query_label, and query_cam for the different methods of re-ID.

Attention：To facilitate the division of thresholds, distances must be normalized to the range of [0, 1].

Save the data format as follows:

mat:{'distmat':distmat,'gallery_label':gallery_label,'gallery_cam':gallery_cam,'query_label':query_label,'query_cam':query_cam} # Save to Mat for testing

Part 2: Testing

In test.py, you need to do the following simple operation:

# Market1501 and DukeMTMC-reID
# Supervised methods
method_name = ['ResNet-50', 'DenseNet-121', 'MLFN', 'HA-CNN', 'MobileNetV2', 'OSNet', 'AlignedReID', 'DG-Net', 'BDB', 'BagTricks', 'FastReID', 'AGW']
line_type = ['-', '--', '-.', '-', '--', '-.', '-', '--', '-.' ,'-', '--', '-.' ]
line_color = ['red', 'green', 'blue', 'tomato', 'sienna', 'darkorange', 'darkgoldenrod', 'gold', 'olive', 'yellow', 'lawngreen', 'palegreen']
line_width = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]

# Unsupervised methods
method_name = ['PUL', 'ECN', 'UDA_TP', 'MMT', 'SpCL']
line_type = ['-', '--', '-.', '-', '--']
line_color = ['red', 'green', 'blue', 'tomato', 'sienna']
line_width = [2, 2, 2, 2, 2]

# ViRi776
method_name = ['ResNet-50', 'VOC-ReID', 'BagTricks']
line_type = ['-', '--', '-.']
line_color = ['red', 'green', 'blue']
line_width = [2, 2, 2]

According to the format and name of re-ID method in part 1. You need to modify the parameters of method_name, line_type and line_width.

Next, you can run the shell:

python test.py

Finally, you can get rank-1, mAP, mVP, mReP, MREP, MFR and the curve of mReP and mFR.

Additional Notes

If you want to test a single method, then only change the method_name to the following example.

method_name = ['ResNet-50']
line_type = ['-']
line_color = ['red']
line_width = [2]

Then call the print_GOM() function to get the following evaluation metrics. See in test.py:

# print rank@1, mAP, mVP_{max}, mReP_{max}, MREP, MFR
gom.print_GOM(method_name, CMC_list, mRP_list, mVP_list, mReP_list, mFR_list, N)

After that, you can get rank@1, mAP, mVP_{max}, mReP_{max}, MREP, MFR.

Comparison with existing metrics using toy samples

A simple re-ID example highlighting the main differences between evaluation metrics. Six different ranking lists are shown. AP and FAR overemphasize the task of retrieval and verification respectively. GOM balances both of these by being an explicit combination of a retrieval precision RP and a verification precision VP, contributed to re-ID precision ReP, and assess the false rate under the different threshold by FR.

Experiment results

The mat format of GOM results is as follows：

GOM_result = {'CMC_list': CMC_list, 'mRP_list': mRP_list, 'mVP_list':mVP_list, 'mReP_list':mReP_list, 'mFR_list':mFR_list}

Download the folder of GOM_results with all the experimental results on Market1501, DukeMTMC-reID and ViRi776 respectively.

# Market1501 and DukeMTMC-reID
# Supervised methods
method_name = ['ResNet-50', 'DenseNet-121', 'MLFN', 'HA-CNN', 'MobileNetV2', 'OSNet', 'AlignedReID', 'DG-Net', 'BDB', 'BagTricks', 'FastReID', 'AGW']

# Unsupervised methods
method_name = ['PUL', 'ECN', 'UDA_TP', 'MMT', 'SpCL']

# ViRi776
method_name = ['ResNet-50', 'VOC-ReID', 'BagTricks']

The corresponding experimental results of curves and values are as follows：

Citation

If you find this code useful for your research, please cite our paper

@article{wang2020gom,
  title={Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric},
  author={Wang, Zheng and Yuan, Xin and Yamasaki, Toshihiko and Lin, Yutian and Xu, Xin and Zeng, Wenjun},
  journal={arXiv preprint arXiv:2011.11506},
  year={2020}
}

person-reid-evaluation's Issues

No such file or directory: '3368_ResNet-50.mat'

Hi Mr Cherry,

I run the test.py, and the code asks me to provide 3368_ResNet-50.mat,
where can I find that, or how can I generate That~?
Thanks~!