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Human Identification at a Distance (HID) Competition This is the official support for Human Identification at a Distance (HID) competition. We provide the baseline code for this competition.

Tutorial for HID 2023

For HID 2023, we will not provide a training set. In this competition, you can use any dataset, such as CASIA-B, OUMVLP, CASIA-E, and/or their own dataset, to train your model. In this tutorial, we will use the model trained on previous HID competition training set as the baseline model.

Download the test set

Download the test gallery and probe from the link. You should decompress these two file by following command:

mkdir hid_2023
tar -zxvf gallery.tar.gz
mv gallery/* hid_2023/
rm gallery -rf
# For Phase 1
tar -zxvf probe_phase1.tar.gz -C hid_2023
mv hid_2023/probe_phase1 hid_2023/probe
# For Phase 2
tar -zxvf probe_phase2.tar.gz -C hid_2023
mv hid_2023/probe_phase2 hid_2023/probe

Download the pretrained model

Download the pretrained model and place it in output after unzipping.

wget https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip
unzip pretrained_hid_model.zip -d output/

Generate the result

Modify the dataset_root in configs/baseline/baseline_hid.yaml, and then run this command:

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/baseline/baseline_hid.yaml --phase test

The result will be generated in HID_result/current_time.csv.

Submit the result

Rename the csv file to submission.csv, then zip it and upload to official submission link. Normally, you should get a score of 48.3 in phase 1.

(Deprecated) Tutorial for HID 2022

We report our result of 68.7% using the baseline model and 80.0% with re-ranking. In order for participants to better start the first step, we provide a tutorial on how to use OpenGait for HID.

Preprocess the dataset

Download the raw dataset from the official link. You will get three compressed files, i.e. train.tar, HID2022_test_gallery.zip and HID2022_test_probe.zip. After unpacking these three files, run this command:

python datasets/HID/pretreatment_HID.py --input_train_path='train' --input_gallery_path='HID2022_test_gallery' --input_probe_path='HID2022_test_probe' --output_path='HID-128-pkl'

Train the dataset

Modify the dataset_root in configs/baseline/baseline_hid.yaml, and then run this command:

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/baseline/baseline_hid.yaml --phase train

You can also download the trained model and place it in output after unzipping.

Get the submission file

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/baseline/baseline_hid.yaml --phase test

The result will be generated in your working directory.

Submit the result

Follow the steps in the official submission guide, you need rename the file to submission.csv and compress it to a zip file. Finally, you can upload the zip file to the official submission link.

Hello! This seems to be a tutorial for the Human Identification at a Distance (HID) competition. How can I assist you with this?

Sorry, I didn't understand your response. Could you please rephrase or provide more information on how I can assist you with the HID competition tutorial?

training set怎么获得的？

For the HID 2023 competition, there will not be a provided training set. Participants can use any dataset, such as CASIA-B, OUMVLP, CASIA-E, and/or their own dataset, to train their model. However, for the HID 2022 competition, you can download the raw dataset from the official link and use the pretreatment_HID.py script to preprocess the data for training.

用中文内容：对于HID 2023比赛，将不提供训练集。参赛者可以使用任何数据集，如CASIA-B、OUMVLP、CASIA-E或他们自己的数据集来训练模型。但是，对于HID 2022比赛，您可以从官方链接下载原始数据集，并使用“pretreatment_HID.py”脚本对数据进行预处理以进行训练。