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update tutorial for hid 2023

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darkliang
2023-02-17 11:46:14 +08:00
parent 76ea237749
commit 0ef6b6f5b4
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datasets/HID/HID2023.json
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datasets/HID/README.md
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# HID Tutorial
# Human Identification at a Distance (HID) Competition
![](http://hid2022.iapr-tc4.org/wp-content/uploads/sites/7/2022/03/%E5%9B%BE%E7%89%871-2.png)
This is the official support for competition of [Human Identification at a Distance (HID)](http://hid2022.iapr-tc4.org/). 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.
This is the official support for [Human Identification at a Distance (HID)](https://hid2023.iapr-tc4.org/) competition. We provide the baseline code for this competition.
## Preprocess the dataset
## 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](https://hid2023.iapr-tc4.org/#:~:text=Dataset%EF%BC%88New%20for%20HID%202023%EF%BC%89).
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](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) 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:
```shell
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](https://codalab.lisn.upsaclay.fr/competitions/10568#participate).
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](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 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
### Train the dataset
Modify the `dataset_root` in `configs/baseline/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 opengait/main.py --cfgs configs/baseline/baseline_hid.yaml --phase train
```
You can also download the [trained model](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) and place it in `output` after unzipping.
## Get the submission file
### Get the submission file
```shell
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
### Submit the result
Follow the steps in the [official submission guide](https://codalab.lisn.upsaclay.fr/competitions/2542#participate), 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](https://codalab.lisn.upsaclay.fr/competitions/2542#participate-submit_results).