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
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 ( |
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 license. Commercial use requires a separate enterprise license from Ultralytics — the ONNX export does not change that.