Object Detection

--- 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.