Initial upload: yolo11n-seg ONNX (apple/fp16/fp8/int8 × 640x640)

README.md

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+---
+license: agpl-3.0
+library_name: onnx
+tags:
+  - yolo
+  - yolov11
+  - object-detection
+  - instance-segmentation
+  - onnx
+  - tensorrt
+pipeline_tag: image-segmentation
+---
+
+# occurra/object_detection_segmentation
+
+ONNX exports of [Ultralytics YOLOv11-seg](https://github.com/ultralytics/ultralytics)
+(instance segmentation) in the configurations the occurra
+`object_detection_segmentation` agent ships with. Companion to
+[`occurra/object_detection`](https://huggingface.co/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](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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