MCFGes

Dataset Description The dataset consists of 105 classes and 2264 samples, collected using an RGB camera and two IWR6843 ISK mmWave radars. The data is stored in a structured format designed to facilitate multimodal and cross-domain few-shot gesture recognition research. Data Organization The dataset file pickle.pkl is organized into a dictionary where each key corresponds to a class_idx. The value associated with each key is a list containing samples from that class. dataset/│├── class_001/│ ├── sample_0001│ ├── sample_0002│ ...│├── class_002/│ ├── sample_0001│ ├── sample_0002│ ...│... Each sample is a dictionary with the following keys: mm_cloud1: Numpy array data from Radar 1, as described in the associated paper. mm_cloud1_mask: Mask data indicating real gesture data versus padding (zeros) for Radar 1. label: The label identifying the gesture class. mm_cloud2: Numpy array data from Radar 2. mm_cloud2_mask: Mask data indicating real gesture data versus padding (zeros) for Radar 2. vis_rgb: Video data from the RGB camera. vis_rgb_mask: Mask data indicating real gesture data versus padding (zeros) for the RGB video. Dataset loading All data undergoes preprocessing and is loaded into batches suitable for model input via the fs_dataset.py script, ensuring that the data is ready for use in machine learning models. Spilit Table The six cross-domain split tables are: split_table_micro.json for MicroGesture(MG) spilt_table_meeting.json for MeetingRoom(MR) split_table_out_door.json for Outdoor(OD) split_table_home.json for Home(H) split_table_static.json for VR(V) split_table_multi_peope.json (MP) Design Patterns in the Project Singleton Pattern: Utilized to manage parameter modules consistently across the project. Readers can adjust settings or introduce new configurations in the config directory to initiate different experiments. Builder Pattern: This pattern is used to enable easy expansion of components such as the training engine, dataset, and model. By adding new build methods, users can enhance the project's capabilities and adapt it to new requirements. Factory Pattern: Supports the registration of new models through decorators in various factories, allowing for the broadening of experimental scope and flexibility. Overall Benefits: These design patterns are implemented to streamline the construction and expansion of experiments, promoting efficient development and scalable architecture. Run: You can edit the script.py and add config file to run your own experiment. Others: There are some git configs and pycache i forget to delete before uploading. Just delete or ignore them. Some __init__.py files contains some import will not be used, just delete them either.数据集描述 本数据集包含105个类别与2264条样本，采集自一台RGB相机与两台IWR6843 ISK型毫米波雷达（mmWave Radar）。数据采用结构化格式存储，旨在为多模态跨域少样本（Few-shot）手势识别研究提供便利。 数据组织方式 数据集文件pickle.pkl以字典形式组织，每个键对应一个类别索引，键对应的值为包含该类别下所有样本的列表。 数据集目录结构如下： dataset/ ├── class_001/ │ ├── sample_0001 │ ├── sample_0002 │ └── ... ├── class_002/ │ ├── sample_0001 │ ├── sample_0002 │ └── ... └── ... 每个样本均为字典格式，包含以下键值： mm_cloud1：来自雷达1的NumPy数组数据，详见相关论文。 mm_cloud1_mask：雷达1的掩码数据，用于区分有效手势数据与填充零值数据。 label：用于标识手势类别的标签。 mm_cloud2：来自雷达2的NumPy数组数据。 mm_cloud2_mask：雷达2的掩码数据，用于区分有效手势数据与填充零值数据。 vis_rgb：RGB相机采集的视频数据。 vis_rgb_mask：RGB视频的掩码数据，用于区分有效手势数据与填充零值数据。 数据集加载 所有数据均经过预处理，可通过fs_dataset.py脚本加载为适配模型输入的批次数据，确保数据可直接用于机器学习模型训练与推理。 划分表 共包含6个跨域划分表： split_table_micro.json：对应微手势（MicroGesture, MG）任务 spilt_table_meeting.json：对应会议室场景（MeetingRoom, MR） split_table_out_door.json：对应户外场景（Outdoor, OD） split_table_home.json：对应居家场景（Home, H） split_table_static.json：对应静态虚拟现实场景（VR, V） split_table_multi_peope.json：对应多人物场景（MP） 项目设计模式 单例模式（Singleton Pattern）：用于在项目中统一管理参数模块。研究人员可通过config目录调整配置或新增配置项，以开展不同的实验。 建造者模式（Builder Pattern）：用于实现训练引擎、数据集、模型等组件的快速扩展。通过新增构建方法，用户可拓展项目功能并适配新的需求。 工厂模式（Factory Pattern）：通过各类工厂的装饰器支持新模型的注册，可拓展实验范围并提升项目灵活性。 整体优势 上述设计模式旨在简化实验的构建与扩展流程，助力高效开发与可扩展架构的搭建。 运行方式 您可编辑script.py脚本并添加配置文件，以运行自定义实验。 其他说明 上传前未清理部分Git配置文件与pycache缓存目录，可直接删除或忽略。 部分__init__.py文件中包含未使用的导入语句，可直接删除。