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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.
|