| --- |
| license: agpl-3.0 |
| tags: |
| - object-detection |
| - yolo |
| - yolo11 |
| - onnx |
| - ultralytics |
| - computer-vision |
| pipeline_tag: object-detection |
| --- |
| |
| # LunchSpot YOLO11n (ONNX) |
|
|
| This repository hosts an **unmodified ONNX export of Ultralytics' YOLO11n** |
| (nano) checkpoint, pretrained on COCO. It is **not a custom-trained or |
| fine-tuned model** — the weights and detection classes are exactly the |
| official Ultralytics release. This repo exists purely as a deployment |
| artifact for the [LunchSpot](https://github.com/<your-org>/lunchspot) |
| project, so the app can pull the model file at build/deploy time instead |
| of committing a ~10 MB binary to the application repository. |
|
|
| If you need the model for a different purpose, prefer the official |
| Ultralytics release (GitHub / PyPI `ultralytics` package) over this mirror. |
|
|
| ## Credits & license |
|
|
| - Original model: [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) |
| - License: **AGPL-3.0**, inherited unchanged from Ultralytics' pretrained |
| weights. Any use of this file is subject to that license (or an |
| Ultralytics Enterprise license if you have one). |
| - No weights were modified. Only the export format changed (PyTorch → ONNX). |
|
|
| ## What this model does |
|
|
| Standard YOLO11n object detector over the 80 COCO classes, exported to |
| ONNX for CPU inference via ONNX Runtime. LunchSpot uses it as-is and, at |
| the application layer, filters detections down to two COCO classes: |
|
|
| | COCO class | Used for | |
| |------------|-----------------------------------| |
| | `person` | occupancy / people counting | |
|
|
|
|
|
|
