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README.md

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+---
+tags:
+- Object detection
+- image classification
+- space utilization analysis
+- Office space management
+- workstation usage analysis
+- smart building
+license: cc-by-nc-sa-4.0
+task_categories:
+- object-detection
+language:
+- en
+pretty_name: Open Office Workstation Usage Detection Dataset
+size_categories:
+- 1B<n<10B
+---
+
+# Open Office Workstation Usage Detection Dataset
+
+A major core advantage of this dataset is its data quality. The annotation process ensures more than 95% accuracy and consistency, covering various lighting conditions and office scenarios. Its technological innovation lies in the use of advanced image enhancement techniques, significantly improving the training effectiveness of detection models. In terms of application value, models trained with this dataset can increase the accuracy of workstation utilization analysis by more than 20% and save over 30% in management costs compared to traditional methods. Compared with other workstation detection datasets, this dataset has outstanding advantages in terms of data volume and diversity, especially with its complete data flow and multi-time period coverage. Unique data features include full coverage of dynamic changes and advanced capabilities for handling complex backgrounds. The dataset structure is optimally designed to support subsequent expansion to other commercial space usage scenarios, and its versatility ensures effective applicability in various office environments.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| occupancy_status | string | Indicates whether the workstation is currently occupied or vacant. |
+| number_of_people | int | The number of people detected within the office area in the image. |
+| desk_count | int | The total number of workstations visible in the image. |
+| lighting_condition | string | The lighting condition within the office, e.g., bright, moderate, dim. |
+| seating_arrangement | string | The arrangement of the workstations, such as open-plan or partitioned. |
+| equipment_count | int | The number of visible equipment in the office area, such as computers, phones, etc. |
+| personal_items_count | int | The number of personal items visible on the workstations, such as mugs, sticky notes, etc. |
+| noise_level | string | The likely noise level as judged visually, such as quiet, moderate, noisy. |
+
+## 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/c2beaec026dff664e8a9a1288fb1edac?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com