Update README.md

README.md

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----
-license: openrail
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: image1
-    dtype: image
-  - name: image2
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 1741995315
-    num_examples: 408
-  download_size: 1712628607
-  dataset_size: 1741995315
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+license: openrail
+tags:
+- computer-vision
+- image-classification
+- object-detection
+- 3d-understanding
+- industrial-design
+- Robotics
+pretty_name: Appliance Knobs
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: image1
+    dtype: image
+  - name: image2
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 1741995315
+    num_examples: 408
+  download_size: 1712628607
+  dataset_size: 1741995315
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+language:
+- en
+size_categories:
+- n<1K
+---
+
+# Codatta Appliance Knobs (Dual-View)
+
+## 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()