darkliang
69 lines
3.8 KiB
Markdown
69 lines
3.8 KiB
Markdown
# Human Identification at a Distance (HID) Competition
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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.
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## Tutorial for HID 2023
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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.
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### Download the test set
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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).
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You should decompress these two file by following command:
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```
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mkdir hid_2023
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tar -zxvf gallery.tar.gz
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mv gallery/* hid_2023/
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rm gallery -rf
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# For Phase 1
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tar -zxvf probe_phase1.tar.gz -C hid_2023
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mv hid_2023/probe_phase1 hid_2023/probe
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# For Phase 2
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tar -zxvf probe_phase2.tar.gz -C hid_2023
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mv hid_2023/probe_phase2 hid_2023/probe
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```
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### Download the pretrained model
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Download the [pretrained model](https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip) and place it in `output` after unzipping.
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```
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wget https://github.com/ShiqiYu/OpenGait/releases/download/v1.1/pretrained_hid_model.zip
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unzip pretrained_hid_model.zip -d output/
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```
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## Generate the result
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Modify the `dataset_root` in `configs/baseline/baseline_hid.yaml`, and then run this command:
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```shell
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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
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```
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The result will be generated in `HID_result/current_time.csv`.
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## Submit the result
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Rename the csv file to `submission.csv`, then zip it and upload to [official submission link](https://codalab.lisn.upsaclay.fr/competitions/10568#participate).
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Normally, you should get a score of **48.3** in phase 1.
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---
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## (Deprecated) Tutorial for HID 2022
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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.
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### Preprocess the dataset
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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`.
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After unpacking these three files, run this command:
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```shell
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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"
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```
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### Train the dataset
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Modify the `dataset_root` in `configs/baseline/baseline_hid.yaml`, and then run this command:
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```shell
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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
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```
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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.
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### Get the submission file
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```shell
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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
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```
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The result will be generated in your working directory.
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### Submit the result
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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). |