From 10045852031814128f01d0742cb2d6471b60c191 Mon Sep 17 00:00:00 2001
From: Dongyang Jin <73057174+jdyjjj@users.noreply.github.com>
Date: Tue, 25 Feb 2025 16:23:29 +0800
Subject: [PATCH] Update README.md

---
 datasets/HID/README.md | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/datasets/HID/README.md b/datasets/HID/README.md
index 67ebe5d..c03f00f 100644
--- a/datasets/HID/README.md
+++ b/datasets/HID/README.md
@@ -1,24 +1,24 @@
 # 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).
\ No newline at end of file
+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).