| --- |
| 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. |
|
|
