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Merge branch 'master' of https://github.com/ShiqiYu/OpenGait

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darkliang
2022-03-11 22:24:35 +08:00
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README.md
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@@ -12,6 +12,8 @@ OpenGait is a flexible and extensible gait recognition project provided by the [
- **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available. - **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available.
- **Nice log**: We use [`tensorboard`](https://pytorch.org/docs/stable/tensorboard.html) and `logging` to log everything, which looks pretty. - **Nice log**: We use [`tensorboard`](https://pytorch.org/docs/stable/tensorboard.html) and `logging` to log everything, which looks pretty.
## What's New
- [Mar 2022] [HID](http://hid2022.iapr-tc4.org/) support is ready in [misc/HID](./misc/HID).
## Model Zoo ## Model Zoo
misc/HID/README.md
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# HID Tutorial # 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. ![](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 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 ## 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`. 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`.
@@ -13,7 +14,7 @@ Modify the `dataset_root` in `./misc/HID/baseline_hid.yaml`, and then run this c
```shell ```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 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. 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 ```shell