Source: how the CamStack YOLO builds are made

This folder is the Corresponding Source under the GNU AGPL-3.0 for the Ultralytics-derived weights in this mirror (objectDetection/yolo26, objectDetection/yolov9, segmentation/yolo26-seg). The full source offer is in THIRD_PARTY_MODELS.md.

File	What it is
`build-camstack-models.py`	Rebuilds every file in the three folders above. One recipe per hosted file stem: upstream checkpoint, input size, the Ultralytics `export()` arguments, the OpenVINO precisions and the INT8 calibration count. Each value is what the hosted file records about itself in its metadata.
`convert_lib.py`	The converter `build-camstack-models.py` calls for OpenVINO: ONNX to IR (`ov.convert_model`, FP32 or FP16-compressed), NNCF INT8 post-training quantisation, and validation of every IR. It is the same file CamStack's Model Studio runs.
`requirements.txt`	Python dependencies of `convert_lib.py`, with per-platform markers.
`export-models.py`	An older bulk exporter for YOLOv8 / YOLOv9 / YOLO11 ids. No file in this mirror needs it.

Rebuilding

python3 -m venv .venv && . .venv/bin/activate
pip install -r requirements.txt ultralytics openvino nncf
python build-camstack-models.py --list            # the recipes: one per hosted file stem
python build-camstack-models.py --check-hosted    # every hosted file is a recipe output?

python build-camstack-models.py --variant camstack-yolo26n-320   # one hosted model, every format
python build-camstack-models.py --family yolov9                  # one mirror folder
python build-camstack-models.py --all                            # the whole set
python build-camstack-models.py --all --formats onnx,openvino    # some formats only

Results go to ./exported-models/<mirror path>, for example exported-models/objectDetection/yolo26/openvino/camstack-yolo26n-320-int8.xml. Each build step runs in its own Python process: on macOS, CoreML, OpenVINO and TensorFlow crash one another when they share one. CoreML needs macOS.

What the recipes encode (the hosted files record each of these):

Folder	Files	ONNX	CoreML	OpenVINO IR
`objectDetection/yolo26`	`camstack-yolo26{n,s,m,l,x}` @640	static, opset 17	static, batch 1	FP16 as `<stem>.xml` and `<stem>-fp16.xml`; INT8 `<stem>-int8.xml` (128 images)
`objectDetection/yolo26`	`camstack-yolo26{n,s,m,l}-{320,256}`	dynamic axes, opset 17	flexible batch 1..16, FP16	FP32 `<stem>.xml`; INT8 `<stem>-int8.xml` (128 images)
`objectDetection/yolov9`	`camstack-yolov9{t,s,m,c}-{320,640}`	static, opset 13	static, batch 1	FP16 `<stem>-fp16.xml`; INT8 `<stem>-int8.xml` (300 images)
`segmentation/yolo26-seg`	`camstack-yolo26{n,s,m}-seg` @640	static, opset 17	static, batch 1	FP16 `<stem>.xml`

Recorded tool versions (Ultralytics / PyTorch / coremltools / OpenVINO): YOLO26 @640 and YOLO26-seg 8.4.24 / 2.10.0 / 9.0 / 2026.2.1; YOLO26 @320 and @256 8.4.92 / 2.13.0 for ONNX, 2.5.1 for CoreML / 8.3.0 / 2026.2.1; YOLOv9 8.4.98 / 2.13.0 / 9.0 / 2026.2.1. The script warns when the installed Ultralytics differs. With these versions a rebuild gives the hosted weights. Other versions give the same weights in a possibly different graph. The flexible-batch CoreML exports need coremltools 8.3 with PyTorch 2.5.1, the macOS pins in requirements.txt; coremltools 9 aborts on them.

INT8 calibration

The INT8 IRs are NNCF post-training quantisation of the ONNX (convert_lib.quantize_openvino_int8), calibrated on COCO images. The documented calibration set is COCO val2017:

curl -LO http://images.cocodataset.org/zips/val2017.zip && unzip -q val2017.zip
python build-camstack-models.py --family yolo26 --calib-dir ./val2017

The recipe takes the first N images in file-name order: 128 for YOLO26, 300 for YOLOv9, as recorded in each IR's rt_info. Each image is letterboxed to the model's input size with grey (114) padding and scaled to [0, 1], the way CamStack feeds the model at runtime. --calib-samples overrides N. An IR does not record WHICH images calibrated it, so a different set gives an equivalent INT8 model, not a bit-identical one. Without calibration images the INT8 files are skipped and the run says so.

Licences

These scripts (build-camstack-models.py, convert_lib.py, export-models.py, requirements.txt) are CamStack's own code and are offered under the MIT licence.
The weights they produce keep their upstream licence. The Ultralytics checkpoints are AGPL-3.0 (LICENSES/AGPL-3.0.txt), and so is every file derived from them in this mirror. Nothing here is relicensed; see LICENSE and each folder's README.md.