Update README.md

README.md

@@ -35,45 +35,48 @@ size_categories:
 
 # Appliance Knobs
 
-## Dataset Summary
+## Overview
 
-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**.
+**Appliance Knobs** is a high-resolution dataset curated by **Codatta**, designed to support fine-grained object understanding, 3D shape estimation, and state recognition tasks.
 
-Curated by **Codatta**, the dataset is designed to support tasks requiring fine-grained object understanding, 3D shape estimation, and state recognition of rotary controls.
+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.
 
-**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.
+The dataset is filtered to ensure high fidelity, making it suitable for industrial design analysis, robotics, and generative AI applications requiring detailed reference material.
 
-## Supported Tasks
+## Dataset Contents
 
-* **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
+Each entry in the dataset consists of a unique identifier and two high-quality images.
 
 ### 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.
+* **`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.
 
-### 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, free from obstruction by hands, wires, or other objects.
+* **Lighting:** Consistent lighting is used to highlight the texture and markings of the controls.
 
-## Quality Standards
+## Key Statistics
 
-* **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.
+* **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 Example
+## Usage
 
-Since this dataset contains paired images, you can load and visualize them side-by-side using Python:
+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
@@ -96,4 +99,8 @@ axes[1].imshow(sample['image2'])
 axes[1].set_title("Side View (Image 2)")
 axes[1].axis('off')
 
-plt.show()
+plt.show()
+```
+
+## License and Open-Source Details
+* **License:** This dataset is released under the **OpenRAIL** license.