darkliang
1.4 KiB
HID Tutorial
This is the official suppor 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.