Publish FactoryVision AI model package (public review)
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README.md
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from ultralytics import YOLO
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model = YOLO('model.pt')
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results = model.predict('your_image.jpg', conf=0.25)
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for result in results:
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print(result.boxes)
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---
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<div align="center">
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<img src="hero.svg" alt="FactoryVision AI PPE detection: YOLO11n object detection for safety equipment" width="100%">
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<p><strong>Educational prototype</strong> | Object detection for factory and construction imagery | Human review required</p>
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</div>
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<h2>Model overview</h2>
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<table>
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<thead><tr><th>Model</th><th>Task</th><th>Framework</th><th>Weights</th></tr></thead>
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<tbody><tr><td>FactoryVision AI PPE YOLO11n</td><td>Object detection</td><td>Ultralytics YOLO11n</td><td><code>model.pt</code></td></tr></tbody>
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</table>
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<h2>Held-out test results</h2>
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<table>
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<thead><tr><th>Metric</th><th>Reported result</th></tr></thead>
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<tbody>
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<tr><td>Precision (bounding boxes)</td><td><strong>0.8400</strong></td></tr>
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<tr><td>Recall (bounding boxes)</td><td><strong>0.6398</strong></td></tr>
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<tr><td>mAP@50</td><td><strong>0.7053</strong></td></tr>
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<tr><td>mAP@50:95</td><td><strong>0.3927</strong></td></tr>
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</tbody>
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</table>
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<p>These values are reported in <code>ppe_test_metrics.json</code>, not independently reproduced. The reported test split has 82 images; a class-specific breakdown is not included in this package. Do not confuse the separate pretrained YOLO COCO person baseline with this PPE result.</p>
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<h2>Purpose and safety</h2>
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<p>Educational object detection for people and PPE-related objects in factory and construction images. <strong>This is not a certified industrial safety system.</strong> Detected PPE status is only a suggested alert requiring human review; it cannot establish a worker's actual PPE compliance or substitute for a trained safety inspector. Missed detections mean unsafe situations may go unflagged.</p>
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<blockquote><strong>Safety limitation:</strong> Object detections alone do not perform the project's person-to-helmet association. The original application implements an additional alert logic layer; consult its source and use human review.</blockquote>
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<h2>Model details</h2>
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<ul>
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<li>Fine-tuned Ultralytics YOLO11n; original starting checkpoint <code>yolo11n.pt</code></li>
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<li>Packaged weights: <code>model.pt</code> (archive path <code>checkpoints/ppe_yolo.pt</code>)</li>
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<li>10 classes: see <code>labels.json</code></li>
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<li>Training configuration: 30 epochs, 640px image size, batch size 16; the training script uses a held-out PPE test split</li>
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</ul>
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<h2>Training data</h2>
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<p>Construction Site Safety Image Dataset, attributed to Snehil Sanyal / Roboflow Universe and referenced by the source project: <a href="https://www.kaggle.com/datasets/snehilsanyal/construction-site-safety-image-dataset-roboflow">Kaggle dataset</a>. The source project lists CC BY 4.0 for this dataset; verify provenance and attribution requirements before public release. Dataset images are not included in this model package.</p>
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<h2>Example inference</h2>
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<pre><code>from ultralytics import YOLO
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model = YOLO('model.pt')
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results = model.predict('your_image.jpg', conf=0.25)
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for result in results:
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print(result.boxes)</code></pre>
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<h2>Licensing and redistribution</h2>
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<ul>
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<li>Ultralytics YOLO11 and its fine-tuned checkpoints have AGPL-3.0 obligations by default, or require a suitable enterprise licence for uses incompatible with AGPL-3.0. See <a href="https://www.ultralytics.com/license">Ultralytics licensing</a>.</li>
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<li>The source repository's MIT licence does <strong>not</strong> automatically license this trained checkpoint, pretrained weights, or source datasets.</li>
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<li>The original developer has reportedly authorised publication; the publisher must still confirm the specific publication terms and applicable third-party rights.</li>
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<li><strong>Do not assign an MIT/Apache model licence or switch to public visibility until licensing has been checked and recorded.</strong></li>
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</ul>
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<h2>Known limitations</h2>
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<p>Domain shift, lighting, small objects, occlusion, crowded scenes, cameras and inference thresholds may reduce performance. False negatives are especially concerning for PPE alerting.</p>
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<hr>
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<h2>Credits and attribution</h2>
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<table>
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<thead><tr><th>Role</th><th>Contributor</th></tr></thead>
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<tbody>
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<tr><td>Original Developer & Model Author</td><td>Theivanth Rajamohan</td></tr>
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<tr><td>Technical Reviewer, Code Contributor & Publication Support</td><td><a href="https://huggingface.co/Swapinvidya">Swapin Vidya</a></td></tr>
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<tr><td>Project Contributor</td><td><a href="https://huggingface.co/rajahshan">Rajah Shanmugasundaram</a></td></tr>
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<tr><td>Publishing Organisation</td><td>TechSphere Consultancy Services Private Limited</td></tr>
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</tbody>
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</table>
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<p><strong>Project provenance:</strong> FactoryVision AI was originally developed by Theivanth Rajamohan. <a href="https://huggingface.co/Swapinvidya">Swapin Vidya</a> contributed technical review, detection-metrics improvements, testing, model publication preparation and documentation. The models are being prepared for publication under TechSphere Consultancy Services Private Limited's Hugging Face namespace, with the original developer's permission.</p>
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<ul>
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<li><a href="https://huggingface.co/TechsphereAI">TechSphereAI on Hugging Face</a></li>
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<li><a href="https://techsphereconsultancy.com/">Official website</a></li>
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<li><a href="https://github.com/Theivanth/Factory-Vision-AI">Original GitHub project</a></li>
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<li><a href="https://github.com/Theivanth/Factory-Vision-AI/pull/1">Swapin Vidya's merged contribution</a></li>
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</ul>
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hero.svg
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