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Dongyang Jin
2025-02-25 16:23:29 +08:00
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# 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 [Human Identification at a Distance (HID)](https://hid2023.iapr-tc4.org/) competition. We provide the baseline code for this competition.
This is the official support for [Human Identification at a Distance (HID)](https://hid2025.iapr-tc4.org/) 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.
## Tutorial for HID 2025
For HID 2025, 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).
Download the test gallery and probe from the [link](https://hid2025.iapr-tc4.org/#:~:text=Dataset%EF%BC%88New%20for%20HID%202025%EF%BC%89).
You should decompress these two file by following command:
```
mkdir hid_2023
mkdir hid_2025
tar -zxvf gallery.tar.gz
mv gallery/* hid_2023/
mv gallery/* hid_2025/
rm gallery -rf
# For Phase 1
tar -zxvf probe_phase1.tar.gz -C hid_2023
mv hid_2023/probe_phase1 hid_2023/probe
tar -zxvf probe_phase1.tar.gz -C hid_2025
mv hid_2025/probe_phase1 hid_2025/probe
# For Phase 2
tar -zxvf probe_phase2.tar.gz -C hid_2023
mv hid_2023/probe_phase2 hid_2023/probe
tar -zxvf probe_phase2.tar.gz -C hid_2025
mv hid_2025/probe_phase2 hid_2025/probe
```
@@ -66,4 +66,4 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node
The result will be generated in your working directory.
### 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).
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).