| --- |
| license: agpl-3.0 |
| library_name: onnx |
| tags: |
| - yolo |
| - yolov11 |
| - object-detection |
| - onnx |
| - tensorrt |
| pipeline_tag: object-detection |
| --- |
| |
| # occurra/object_detection |
| |
| ONNX exports of [Ultralytics YOLOv11](https://github.com/ultralytics/ultralytics) |
| in the configurations the occurra `object_detection` agent ships with. |
|
|
| Two model sizes (nano `n`, small `s`), four precision variants, and two |
| input resolutions. All files are self-contained (no external-data |
| sidecars). |
|
|
| ## Filename convention |
|
|
| ``` |
| yolo11{n,s}_{apple,fp16,fp8,int8}_{640x640,640x480}.onnx |
| ``` |
|
|
| | Token | Meaning | |
| | ----- | ------- | |
| | `n` / `s` | YOLOv11 nano (~5 MB) or small (~19 MB) | |
| | `apple` | FP16, NMS-free, batch=1, static — CoreML / Apple ANE friendly. uint8 input. | |
| | `fp16` | FP16 weights, NMS embedded. Default for NVIDIA `TensorRT` EP. | |
| | `fp8` | FP8 quantized via TensorRT QDQ. Smallest VRAM footprint on Blackwell / Hopper. | |
| | `int8` | INT8 quantized with QDQ nodes embedded in the graph. No sidecar calibration cache needed. | |
| | `640x640` | Square input — used on Apple (`apple` variant) and as the upstream default. | |
| | `640x480` | 4:3 input — ~25% fewer FLOPs than 640×640 on cameras with 4:3 aspect, measurably faster on NVIDIA TensorRT. | |
|
|
| The `object_detection` agent reads the input shape directly from the |
| loaded ONNX (`graph.input[0].type`) — no sidecar config, the file name |
| is informational. |
|
|
| ## Which file to pick |
|
|
| | Hardware | Recommended | |
| | -------- | ----------- | |
| | Apple Silicon (CoreML / ANE) | `yolo11n_apple_640x640.onnx` | |
| | NVIDIA RTX 4000+ / Blackwell | `yolo11n_fp8_640x480.onnx` | |
| | NVIDIA older (no FP8) | `yolo11n_int8_640x480.onnx` or `yolo11n_fp16_640x480.onnx` | |
| | Higher accuracy (any NVIDIA) | swap the `n` for `s` (3–4× slower, marginally better mAP) | |
|
|
| ## Source |
|
|
| Trained Ultralytics checkpoints (`yolo11n.pt`, `yolo11s.pt`) are |
| downloaded from Ultralytics' release feed and re-exported via the |
| `occurra` toolbox's |
| `ai_agent_toolbox/agents/python/object_detection/scripts/main.py` |
| (NMS-free for Apple, with-NMS for NVIDIA; FP8/INT8 use TensorRT QDQ). |
|
|
| ## License |
|
|
| The model weights inherit Ultralytics YOLOv11's |
| [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) |
| license. Commercial use requires a separate enterprise license from |
| Ultralytics — the ONNX export does not change that. |
|
|
