Publish FactoryVision AI model package (public review)

README.md

@@ -10,43 +10,45 @@ tags:
 - education
 - yolo11
 ---
-# FactoryVision AI — PPE YOLO11n (fine-tuned)
+# 🏭 FactoryVision AI
+## PPE Detection · YOLO11n
 
-**FactoryVision AI — Credits and Attribution**
+> **Educational prototype** · Object detection for factory and construction imagery · **Human review required**
 
-**Original Developer & Model Author:** Theivanth Rajamohan  
-**Technical Review, Code Contributions & Model Publication Support:** [Swapin Vidya](https://huggingface.co/Swapinvidya)  
-**Project Contributor:** [Rajah Shanmugasundaram](https://huggingface.co/rajahshan)  
-**Publishing Organisation:** TechSphere Consultancy Services Private Limited  
-**Hugging Face:** https://huggingface.co/TechsphereAI  
-**Official Website:** https://techsphereconsultancy.com/
+| Model | Task | Framework | Weights |
+|---|---|---|---|
+| FactoryVision AI PPE YOLO11n | Object detection | Ultralytics YOLO11n | `model.pt` |
 
-**Project Provenance:** FactoryVision AI was originally developed by Theivanth Rajamohan. [Swapin Vidya](https://huggingface.co/Swapinvidya) 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.
+## 📊 Held-out test results
 
-**Original GitHub Repository:** https://github.com/Theivanth/Factory-Vision-AI  
-**Merged contribution by [Swapin Vidya](https://huggingface.co/Swapinvidya):** https://github.com/Theivanth/Factory-Vision-AI/pull/1
+| Metric | Reported result |
+|---|---:|
+| Precision (bounding boxes) | **0.8400** |
+| Recall (bounding boxes) | **0.6398** |
+| mAP@50 | **0.7053** |
+| mAP@50:95 | **0.3927** |
 
-## Purpose and limitations
-Educational object detection for people and PPE-related objects in factory/construction images. **Not a certified industrial safety system.** 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. In particular, missed detections mean unsafe situations may go unflagged.
+These values are reported in `ppe_test_metrics.json`, 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.
+
+## 🎯 Purpose and safety
+
+Educational object detection for people and PPE-related objects in factory/construction images. **This is not a certified industrial safety system.** 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.
+
+> ⚠️ 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.
+
+## 🧠 Model details
 
-## Architecture and files
 - Fine-tuned Ultralytics YOLO11n; original starting checkpoint `yolo11n.pt`
-- Weights: `model.pt` (archive `checkpoints/ppe_yolo.pt`)
+- Packaged weights: `model.pt` (archive path `checkpoints/ppe_yolo.pt`)
 - 10 classes: see `labels.json`
-- Training: 30 configured epochs, 640px image size, batch size 16; the training script uses a held-out PPE test split
+- Training configuration: 30 epochs, 640px image size, batch size 16; the training script uses a held-out PPE test split
 
-## Training data
-Construction Site Safety Image Dataset, attributed to Snehil Sanyal / Roboflow Universe, referenced by the source project: https://www.kaggle.com/datasets/snehilsanyal/construction-site-safety-image-dataset-roboflow . 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.
+## 🗂️ Training data
 
-## Held-out test performance (from the supplied Kaggle output)
-- Precision (bounding boxes): **0.8400**
-- Recall (bounding boxes): **0.6398**
-- mAP@50: **0.7053**
-- mAP@50:95: **0.3927**
+Construction Site Safety Image Dataset, attributed to Snehil Sanyal / Roboflow Universe and referenced by the source project: [Kaggle dataset](https://www.kaggle.com/datasets/snehilsanyal/construction-site-safety-image-dataset-roboflow). 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.
 
-The numbers above are reported by the training artefact `ppe_test_metrics.json`, not independently reproduced. The reported test split has 82 images; class-specific breakdown is not included in this package. Do not confuse the separate pretrained YOLO COCO person baseline with this PPE result.
+## 🚀 Example inference
 
-## Example inference
 ```python
 from ultralytics import YOLO
 model = YOLO('model.pt')
@@ -55,13 +57,31 @@ for result in results:
     print(result.boxes)
 ```
 
-Note: 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.
+## ⚖️ Licensing and redistribution
 
-## Licensing and redistribution
-- 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 https://www.ultralytics.com/license
+- 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 [Ultralytics licensing](https://www.ultralytics.com/license).
 - The source repository's MIT licence does **not** automatically license this trained checkpoint, pretrained weights, or source datasets.
 - The original developer has reportedly authorised publication; the publisher must still confirm the specific publication terms and applicable third-party rights.
 - **Do not assign an MIT/Apache model licence or switch to public visibility until licensing has been checked and recorded.**
 
-## Known limitations
+## ⚠️ Known limitations
+
 Domain shift, lighting, small objects, occlusion, crowded scenes, cameras and inference thresholds may reduce performance. False negatives are especially concerning for PPE alerting.
+
+---
+
+## 🤝 Credits and attribution
+
+| Role | Contributor |
+|---|---|
+| Original Developer & Model Author | Theivanth Rajamohan |
+| Technical Reviewer, Code Contributor & Publication Support | [Swapin Vidya](https://huggingface.co/Swapinvidya) |
+| Project Contributor | [Rajah Shanmugasundaram](https://huggingface.co/rajahshan) |
+| Publishing Organisation | TechSphere Consultancy Services Private Limited |
+
+**Project provenance:** FactoryVision AI was originally developed by Theivanth Rajamohan. [Swapin Vidya](https://huggingface.co/Swapinvidya) 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.
+
+- 🤗 [TechSphereAI on Hugging Face](https://huggingface.co/TechsphereAI)
+- 🌐 [Official website](https://techsphereconsultancy.com/)
+- 💻 [Original GitHub project](https://github.com/Theivanth/Factory-Vision-AI)
+- 🔀 [Swapin Vidya's merged contribution](https://github.com/Theivanth/Factory-Vision-AI/pull/1)