Appliance-Knobs

# Appliance Knobs

## Dataset Summary

This dataset contains a high-resolution collection of electrical appliance knobs and rotary controls. Each data entry consists of a **paired image set** capturing the same knob from two distinct angles: **Front View** and **Side View**.

Curated by **Codatta**, the dataset is designed to support tasks requiring fine-grained object understanding, 3D shape estimation, and state recognition of rotary controls.

**Key Features:**
* **Dual-View:** Every knob is captured from both the front (`image1`) and the side (`image2`).
* **High Quality:** Images are filtered to ensure they are clear, focused, and free from occlusion.
* **Resolution:** The dataset size (~1.7GB for 408 pairs) indicates high-fidelity imaging suitable for detailed analysis.

## 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.

## Dataset Structure

### Data Fields

The dataset features are structured as follows:

* **`id`** (string): Unique identifier for the knob/appliance sample.
* **`image1`** (image): **Front View**. A direct frontal shot of the knob, showing the face, markings, and position indicators clearly.
* **`image2`** (image): **Side View**. A profile or oblique angle shot of the same knob to showcase its height, depth, material texture, and grip patterns.

### Data Preview
*(The Hugging Face viewer will automatically render the images below)*

## Quality Standards

* **Clear & Unoccluded:** All images have been manually verified to ensure the knob is the primary focus, without obstruction by hands, wires, or other objects.
* **Lighting:** Consistent lighting was used to highlight the texture and markings of the controls.

## Usage Example

Since this dataset contains paired images, you can load and visualize them side-by-side using Python:

```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()

# 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()