Appliance-Knobs
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# Appliance Knobs ## Overview **Appliance Knobs** is a high-resolution dataset curated by **Codatta**, designed to support fine-grained object understanding, 3D shape estimation, and state recognition tasks. This collection focuses on electrical appliance knobs and rotary controls. Its defining feature is the **paired image set** structure: every data entry captures the same specific knob from two distinct and correlated angles: * **Front View:** A direct shot showing indicators and markings. * **Side View:** A profile shot showing depth, height, and texture. The dataset is filtered to ensure high fidelity, making it suitable for industrial design analysis, robotics, and generative AI applications requiring detailed reference material. ## Dataset Contents Each entry in the dataset consists of a unique identifier and two high-quality images. ### Data Fields * **`id`** (string): Unique identifier for the knob/appliance sample. * **`image1`** (image): **Front View**. A direct frontal shot of the knob, clearly showing the face, markings, and position indicators. * **`image2`** (image): **Side View**. A profile or oblique angle shot of the same knob to showcase its height, depth, material texture, and grip patterns. ### Quality Standards * **Clear & Unoccluded:** All images have been manually verified to ensure the knob is the primary focus, free from obstruction by hands, wires, or other objects. * **Lighting:** Consistent lighting is used to highlight the texture and markings of the controls. ## Key Statistics * **Total Examples:** 408 paired samples. * **Dataset Size:** ~1.74 GB (indicating high-resolution imagery). * **Views per Sample:** 2 (Front and Side). * **Language:** English (`en`). ## Usage This dataset is optimized for tasks that benefit from multi-view correlation and high-resolution texture details. **Supported Tasks:** * **Multi-View Object Recognition:** Identifying objects using correlated information from different viewpoints. * **3D Shape Reconstruction:** Inferring the 3D structure and depth of knobs based on the front and side profiles. * **Knob State/Angle Estimation:** Training models to read the precise setting or angle of a dial. * **Generative AI Training:** Serving as high-quality reference data for training LoRAs or ControlNets for specific industrial components. **Python Usage Example:** You can load and visualize the paired images side-by-side using the following code: ```python from datasets import load_dataset import matplotlib.pyplot as plt # Load the dataset ds = load_dataset("Codatta/appliance-knobs-dual-view", split="train") # Get a sample sample = ds[0] # Visualize Front vs Side view fig, axes = plt.subplots(1, 2, figsize=(10, 5)) axes[0].imshow(sample['image1']) axes[0].set_title("Front View (Image 1)") axes[0].axis('off') axes[1].imshow(sample['image2']) axes[1].set_title("Side View (Image 2)") axes[1].axis('off') plt.show() ``` ## License and Open-Source Details * **License:** This dataset is released under the **OpenRAIL** license.
# 家电旋钮（Appliance Knobs） ## 数据集摘要 本数据集收录了一组高分辨率的家电旋钮与旋转控制器样本。每条数据均包含**配对图像集**，从两个独特视角拍摄同一旋钮：**正视图**与**侧视图**。 该数据集由Codatta整理，旨在支持针对旋转控制器的细粒度物体理解、三维形状估计与状态识别等相关任务。 ### 核心特性 * **双视角采集：** 每个旋钮均从正面（`image1`）与侧面（`image2`）完成拍摄。 * **高质量成像：** 所有图像均经过筛选，确保清晰对焦且无遮挡问题。 * **高分辨率规格：** 数据集总大小约1.7GB，包含408组样本，足以支撑精细化分析所需的高保真成像需求。 ## 支持任务 * **多视角物体识别：** 利用不同视角的关联信息完成物体识别。 * **三维形状重建：** 基于正、侧轮廓推断旋钮的三维结构与深度信息。 * **旋钮状态/角度估计：** 训练模型以读取旋钮的精确设置或旋转角度。 * **生成式AI训练：** 可作为高质量参考数据，用于训练针对特定工业部件的LoRA或ControlNets。 ## 数据集结构 ### 数据字段 本数据集的特征结构如下： * **`id`** (string)：旋钮/家电样本的唯一标识符。 * **`image1`** (图像)：**正视图**。旋钮的正面直拍图像，可清晰展示旋钮盘面、标识与位置指示器。 * **`image2`** (图像)：**侧视图**。同一旋钮的侧面或斜拍图像，用于展示其高度、深度、材质纹理与防滑纹路。 ### 数据预览 *(Hugging Face查看器将自动渲染下方图像)* ## 质量标准 * **清晰无遮挡：** 所有图像均经过人工核验，确保旋钮为画面主体，未被手部、线材或其他物体遮挡。 * **光照统一：** 采用标准化光照条件，以突出控制器的纹理与标识细节。 ## 使用示例 由于本数据集包含配对图像，你可以使用Python代码加载并并排可视化它们： python from datasets import load_dataset import matplotlib.pyplot as plt # 加载数据集 ds = load_dataset("Codatta/appliance-knobs-dual-view", split="train") # 获取单条样本 sample = ds[0] # 可视化正视图与侧视图 fig, axes = plt.subplots(1, 2, figsize=(10, 5)) axes[0].imshow(sample['image1']) axes[0].set_title("正视图（图像1）") axes[0].axis('off') axes[1].imshow(sample['image2']) axes[1].set_title("侧视图（图像2）") axes[1].axis('off') plt.show()
