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README.md

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+---
+tags:
+- Image Classification
+- Anomaly Detection
+- Industrial Inspection
+- Quality Control
+license: cc-by-nc-sa-4.0
+task_categories:
+- image-classification
+language:
+- en
+pretty_name: Generator Inspection Dataset
+size_categories:
+- 1B<n<10B
+---
+
+# Generator Inspection Dataset
+
+Currently, the industrial sector faces significant challenges in ensuring the quality and safety of generator operations. With the increasing complexity of generator designs, timely and accurate inspection becomes paramount. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error. This dataset aims to address the need for automated inspection techniques by providing a comprehensive collection of images representing various generator models under different conditions. Data collection involved using high-resolution cameras in controlled environments, ensuring optimal lighting and focus. Quality control measures include multi-round annotations and expert reviews to guarantee labeling accuracy. The dataset is organized in JPG format for easy access and integration into machine learning workflows. 
+The core advantages of this dataset lie in its high-quality annotations and innovative data collection methods. Each image is labeled with a unique identifier and classification, ensuring consistency and completeness. The dataset’s validation process has achieved an annotation accuracy of over 95%, significantly reducing errors compared to existing datasets. Furthermore, the inclusion of diverse generator models enhances its application value, enabling improved performance in anomaly detection tasks, with potential performance gains of up to 20% in model accuracy when utilized in training.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| generator_model | string | Specific model number of the generator |
+| defect_presence | boolean | Whether there are noticeable external defects in the image |
+| defect_type | string | Types of defects identified in the image, such as scratches, cracks, etc. |
+| body_color | string | Color of the generator body |
+| logo_presence | boolean | Whether the brand label of the generator is visible in the image |
+| inspection_environment | string | Environmental information during image capture, such as indoor or outdoor |
+| view_angle | string | The angle of view from which an image is captured, such as front, side, etc. |
+| background_clutter | string | The level of clutter in the image's background, with possible values being high, medium, low. |
+
+## 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/c1bbb205c3198a33276b115283b4385c?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com