This is the official support for competition of Human Identification at a Distance (HID). We report our result is 68.7% using the baseline model. 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 misc/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 ./misc/HID/baseline_hid.yaml, and then run this command:

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

The result will be generated in your working directory, you must rename and compress it as the requirements before submitting.