Initial upload: YOLOv11 ONNX (nano + small × {apple,fp16,fp8,int8} × {640x640,640x480})

README.md

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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.

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