support HID

2022-03-11 19:32:23 +08:00
parent 7c33b33440
commit 7d7b81f48e
5 changed files with 1281 additions and 0 deletions
@@ -0,0 +1,22 @@
+# 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](http://hid2022.iapr-tc4.org/). 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:
+```shell
+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:
+```shell
+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](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.pt) and place it in `output` after unzipping.
+
+## Get the submission file.
+```shell
+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.