occurra/object_detection_segmentation

ONNX exports of Ultralytics YOLOv11-seg (instance segmentation) in the configurations the occurra object_detection_segmentation agent ships with. Companion to occurra/object_detection — same class set (person + bicycle + 4 vehicle subtypes), same naming convention, same hardware-selection logic, with per-object pixel masks on top of bounding boxes.

Nano size only (no small variant yet). Four precision variants. All files are self-contained (no external-data sidecars).

Filename convention

yolo11n-seg_{apple,fp16,fp8,int8}_640x640.onnx

Token	Meaning
n-seg	YOLOv11 nano segmentation variant
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 — same shape used by the upstream Ultralytics export.

The object_detection_segmentation 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-seg_apple_640x640.onnx
NVIDIA RTX 4000+ / Blackwell	yolo11n-seg_fp8_640x640.onnx
NVIDIA older (no FP8)	yolo11n-seg_int8_640x640.onnx
CPU fallback	yolo11n-seg_fp16_640x640.onnx

The agent's _resolve_model_filename picks automatically based on platform + GPU compute capability. Set OBJECT_DETECTION_SEGMENTATION_MODEL=<filename> to force a specific variant.

Outputs

Each ONNX has two outputs (Ultralytics-seg standard):

Output	Shape	Contents
output0	(batch, 4+80+32, N)	[cx, cy, w, h] + 80 class scores + 32 mask coefficients per anchor
output1	(batch, 32, proto_h, proto_w)	Prototype masks; coeffs @ protos reconstructs the per-detection mask.

The agent runs NMS in Python after filtering to the curated class set (COCO 0/1/2/3/5/7 → person, bicycle, car, motorcycle, bus, truck) and decodes masks in YoloSegOnnx. Bitplane bytes are passed to the C++ toolbox for denoising + RLE encoding.

Source

Ultralytics yolo11n-seg.pt checkpoints downloaded from Ultralytics' release feed and re-exported via the occurra toolbox's ai_agent_toolbox/agents/python/object_detection_segmentation/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.