From 6db0125ee58b7d7f1b368f598d247bbc651a92fd Mon Sep 17 00:00:00 2001 From: Jianfei Yang Date: Tue, 12 Jul 2022 14:19:29 +0800 Subject: [PATCH] Update README.md --- README.md | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 08600be..7c36f72 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,7 @@ +[![GitHub](https://img.shields.io/github/license/Marsrocky/Awesome-WiFi-CSI-Sensing?color=blue)](https://github.com/Marsrocky/Awesome-WiFi-CSI-Sensing/blob/main/LICENSE) +[![Maintenance](https://img.shields.io/badge/Maintained%3F-YES-green.svg)](https://github.com/Marsrocky/Awesome-WiFi-CSI-Sensing/graphs/commit-activity) +![Ask Me Anything !](https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg) + # SenseFi: A Benchmark for WiFi CSI Sensing ## Introduction SenseFi is the first open-source benchmark and library for WiFi CSI human sensing, implemented by PyTorch. The state-of-the-art networks, including MLP, CNN, RNN, Transformers, etc, are evaluated on four public datasets across different WiFi CSI platforms. The details are illustrated in our paper [*Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial*](). @@ -55,8 +59,8 @@ You can choose [dataset name] from the dataset list below - Widar *Example: `python run.py --model ResNet18 --dataset NTU-Fi_HAR`* -### Self-supervised Learning -To run models with self-supervised learning (train & test): +### Unsupervised Learning +To run models with unsupervised (self-supervised) learning (train on **NTU-Fi HAR** & test on **NTU-Fi HumanID**): Run: `python self_supervised.py --model [model name] ` You can choose [model name] from the model list below @@ -112,35 +116,36 @@ You can choose [model name] from the model list below ## Dataset #### UT-HAR -- **size** : 1 x 250 x 90 +[*A Survey on Behavior Recognition Using WiFi Channel State Information*](https://ieeexplore.ieee.org/document/8067693) [[Github]](https://github.com/ermongroup/Wifi_Activity_Recognition) +- **CSI size** : 1 x 250 x 90 - **number of classes** : 7 - **classes** : lie down, fall, walk, pickup, run, sit down, stand up - **train number** : 3977 - **test number** : 996 -[*A Survey on Behavior Recognition Using WiFi Channel State Information*](https://ieeexplore.ieee.org/document/8067693) [[Github]](https://github.com/ermongroup/Wifi_Activity_Recognition) #### NTU-HAR -- **size** : 3 x 114 x 500 +[*Efficientfi: Towards Large-Scale Lightweight Wifi Sensing via CSI Compression*](https://arxiv.org/abs/2204.04138) +- **CSI size** : 3 x 114 x 500 - **number of classes** : 6 - **classes** : box, circle, clean, fall, run, walk - **train number** : 936 - **test number** : 264 -[*Efficientfi: Towards Large-Scale Lightweight Wifi Sensing via CSI Compression*](https://arxiv.org/abs/2204.04138) #### NTU-HumanID -- **size** : 3 x 114 x 500 +[*CAUTION: A Robust WiFi-based Human Authentication System via Few-shot Open-set Gait Recognition*](https://ieeexplore.ieee.org/abstract/document/9726794) +- **CSI size** : 3 x 114 x 500 - **number of classes** : 14 - **classes** : gaits of 14 subjects - **train number** : 546 - **test number** : 294 -[*CAUTION: A Robust WiFi-based Human Authentication System via Few-shot Open-set Gait Recognition*](https://ieeexplore.ieee.org/abstract/document/9726794) *Examples of NTU-Fi data* #### Widar -- **size** : 22 x 20 x 20 +[*Widar3.0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi*](https://ieeexplore.ieee.org/document/9516988) [[Project]](http://tns.thss.tsinghua.edu.cn/widar3.0/) +- **BVP size** : 22 x 20 x 20 - **number of classes** : 22 - **classes** : Push&Pull, Sweep, Clap, Slide, Draw-N(H), Draw-O(H),Draw-Rectangle(H), @@ -151,7 +156,7 @@ Draw-2, Draw-3, Draw-4, Draw-5, Draw-6, Draw-7, Draw-8, Draw-9, Draw-10 *Classes of Widar data* -[*Widar3.0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi*](https://ieeexplore.ieee.org/document/9516988) [[Project]](http://tns.thss.tsinghua.edu.cn/widar3.0/) + ## Datasets Reference ```