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README.md

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+---
+tags:
+- object recognition
+- image classification
+- placement detection
+- scene understanding
+- smart home
+- machine vision
+- indoor navigation
+- home automation
+license: cc-by-nc-sa-4.0
+task_categories:
+- object-detection
+language:
+- en
+pretty_name: Kitchen Tableware Placement Image Dataset
+size_categories:
+- 1B<n<10B
+---
+
+# Kitchen Tableware Placement Image Dataset
+
+The current smart home industry is developing rapidly, but challenges remain in object recognition and dynamic detection. Many existing solutions lack accuracy in recognition and understanding of complex placement scenarios. This dataset aims to improve the accuracy and efficiency of object recognition in home scenarios to meet the demand for high-precision data in smart homes. Data is collected by high-resolution cameras in various home environments, covering different times and lighting conditions. Quality control includes multiple rounds of manual annotation and expert review to ensure accuracy and consistency, carried out by a team of ten with computer vision backgrounds. Data preprocessing methods include image enhancement and normalization, stored in JPG format for easy retrieval and use.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| object_type | string | The category of kitchen items in the image, such as pots, bowls, chopsticks, etc. |
+| object_color | string | The primary color of the item identified in the image. |
+| object_size | string | The size or volume of the item identified in the image, such as large, medium, small. |
+| position_in_kitchen | string | The specific placement of the item in the kitchen, such as on the counter, on the wall, in a drawer, etc. |
+| object_orientation | string | The orientation of the item in the image, such as front, side, back, etc. |
+| material_type | string | The material type of the item in the image, such as glass, metal, plastic, etc. |
+| image_quality | string | The quality level of the image, such as high-definition, blurry, overexposed, etc. |
+| light_condition | string | The lighting conditions during the image capture, such as bright, shadow, backlit, etc. |
+| background_type | string | The background type of the image, such as monochromatic, cluttered, kitchen scene, etc. |
+
+## Compliance Statement
+
+<table>
+  <tr>
+    <td>Authorization Type</td>
+    <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td>
+  </tr>
+  <tr>
+    <td>Commercial Use</td>
+    <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td>
+  </tr>
+  <tr>
+    <td>Privacy and Anonymization</td>
+    <td>No PII, no real company names, simulated scenarios follow industry standards</td>
+  </tr>
+  <tr>
+    <td>Compliance System</td>
+    <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td>
+  </tr>
+</table>
+
+## Source & Contact
+
+If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/65477e19b8f019c7d7746d6de878676c?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com