GaitSet

A flexible, effective and fast network for cross-view gait recognition. It consistent with the results in GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition

We arrived Rank@1=95.0% on CASIA-B and Rank@1=87.1% on OU-MVLP.

What's new

Update the organization of the dataset directory. See Dataset & Preparation. You might have to change your dataset_path in config.py.
Add a new arg (cache) in both training and test. See Train & Test

Prerequisites

Python 3.6
PyTorch 0.4+
GPU

Getting started

Installation

(Not necessary) Install Anaconda3
Install CUDA 9.0
install cuDNN7.0
Install PyTorch

Noted that our code is tested based on PyTorch 0.4

Dataset & Preparation

Download CASIA-B Dataset

***ATTENTION***

Organize the directory as: your_dataset_path/subject_ids/walking_conditions/views. E.g. CASIA-B/001/nm-01/000/.
You should cut and align the raw silhouette by yourself. Our experiments use the align method in this paper.
The resolution of the sample should be $64\times64$

Futhermore, you also can test our code on OU-MVLP Dataset. The number of channels and the training batchsize is slightly different for this dataset. For more detail, please refer to our paper.

Configuration

In config.py, you might want to change the following settings:

WORK_PATH path to save/load checkpoints
CUDA_VISIBLE_DEVICES indices of GPUs
dataset_path (necessary) root path of the dataset (for the above example, it is "gaitdata")

Train

Train a model by

python train.py

--cache if set as TRUE all the training data will be loaded at once before the training start. This will accelerate the training. Note that if this arg is set as FALSE, samples will NOT be kept in the memory even they have been used in the former iterations. #Default: TRUE

Test & Evaluation

Use trained model to extract feature by

python test.py

--iter iteration of the checkpoint to load. #Default: 80000
--batch_size batch size of the parallel test. #Default: 1
--cache if set as TRUE all the test data will be loaded at once before the transforming start. This might accelerate the testing. #Default: FALSE

It will output Rank@1 of all three walking conditions. Note that the test is parallelizable. To conduct a faster evaluation, you could use --batch_size to change the batch size for test.

ligftffjjfh / GaitSet