THIRD_PARTY_MODELS.md

Third-party models

CamStack's own source code is MIT-licensed (see LICENSE). CamStack does not include model weights in its npm packages or its Docker image. It downloads them, on demand, from the locations listed below. Each model stays under its own licence. Converting a model to another format does not change its licence: every CamStack-made build is a modified version (a "model derivative") of the upstream weights, and the change is stated per model.

Full licence texts: licenses/models/ in the CamStack source repository, and LICENSES/ in the model mirror https://huggingface.co/camstack/camstack-models.

Some of these models are not licensed for commercial use. They are listed under Non-commercial models.

Required attributions

• © Ultralytics, AGPL-3.0. Corresponding source: the upstream Ultralytics checkpoint and build-camstack-models.py (with convert_lib.py) published at https://huggingface.co/camstack/camstack-models/tree/main/source. https://github.com/ultralytics/ultralytics
• Coral Edge TPU models and AIY Vision Birds V1 © Google LLC, Apache-2.0. https://github.com/google-coral/test_data
• © Ultralytics, AGPL-3.0 (Ultralytics YOLOv9 weights). YOLOv9 architecture: Chien-Yao Wang, I-Hau Yeh, Hong-Yuan Mark Liao, GPL-3.0 (https://github.com/WongKinYiu/yolov9). Corresponding source: the upstream Ultralytics checkpoint and build-camstack-models.py (with convert_lib.py) published at https://huggingface.co/camstack/camstack-models/tree/main/source. https://github.com/ultralytics/ultralytics
• RF-DETR © Roboflow, Apache-2.0. https://github.com/roboflow/rf-detr
• YuNet weights Copyright (c) 2020 Shiqi Yu, MIT. Training code: ShiqiYu/libfacedetection.train, BSD-3-Clause. https://github.com/opencv/opencv_zoo/tree/main/models/face_detection_yunet
• AuraFace v1 by fal.ai, Apache-2.0. https://huggingface.co/fal/AuraFace-v1
• open-lpr models, Copyright the open-lpr contributors, Apache-2.0 (https://huggingface.co/faisalthaheem/open-lpr-models, tag detector-2026.10.1). YOLOX architecture and COCO pretraining by Megvii (https://github.com/Megvii-BaseDetection/YOLOX), Apache-2.0. The upstream NOTICE is reproduced verbatim (NOTICE-open-lpr.txt). https://huggingface.co/faisalthaheem/open-lpr-models
• fast-plate-ocr, MIT License, copyright (c) 2024 ankandrew (full notice: licenses/models/MIT-fast-plate-ocr.txt). https://github.com/ankandrew/fast-plate-ocr
• EfficientNet-Lite0 via timm (Ross Wightman), Apache-2.0. Open Images V7 annotations © Google LLC, CC BY 4.0; images listed by Google as CC BY 2.0, without warranty. https://github.com/huggingface/pytorch-image-models
• torchvision MobileNetV3, BSD-3-Clause. Trained on the Vehicle classification dataset by DrBimmer (https://huggingface.co/datasets/DrBimmer/vehicle-classification), CC BY-NC 4.0. https://github.com/pytorch/vision
• SigLIP 2 © Google LLC, Apache-2.0. https://huggingface.co/google/siglip2-base-patch16-224
• U²-Net © Xuebin Qin et al., Apache-2.0. https://github.com/xuebinqin/U-2-Net
• YAMNet © Google LLC, Apache-2.0. AudioSet ontology and labels © Google LLC, CC BY 4.0. https://github.com/tensorflow/models/tree/master/research/audioset/yamnet
• Qwen models © Alibaba Cloud, Apache-2.0. https://huggingface.co/Qwen · https://huggingface.co/unsloth · https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF
• Built with Llama. Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF

Training data under attribution terms:

• COCO 2017 (training data of yolo26n and 50 more): annotations CC-BY-4.0; images under their Flickr owners' terms. https://cocodataset.org/#termsofuse
• WIDER FACE (training data of yunet-2023mar): research-oriented terms; images CC BY-NC-ND. http://shuoyang1213.me/WIDERFACE/
• Open Images V7 (training data of animal-classifier): annotations CC-BY-4.0 (Google LLC); images listed as CC-BY-2.0, no warranty. https://storage.googleapis.com/openimages/web/factsfigures_v7.html
• DrBimmer/vehicle-classification (training data of vehicle-type-v1): CC-BY-NC-4.0 — treated as binding the trained weights (conservative reading). https://huggingface.co/datasets/DrBimmer/vehicle-classification
• AudioSet ontology/labels (training data of yamnet-onnx): CC-BY-4.0 (Google LLC). https://research.google.com/audioset/

Source offer for the AGPL-3.0 weights

The folders objectDetection/yolo26, objectDetection/yolov9 and segmentation/yolo26-seg of the model mirror hold Ultralytics weights, modified by CamStack and distributed under AGPL-3.0. Their Corresponding Source is published in the mirror itself, in its source/ folder, https://huggingface.co/camstack/camstack-models/tree/main/source, together with the upstream checkpoints it starts from:

1. The upstream checkpoints. Ultralytics publishes yolo26{n,s,m,l,x}.pt, yolov9{t,s,m,c}.pt and yolo26{n,s,m}-seg.pt at https://github.com/ultralytics/assets/releases.
2. The build script in source/: build-camstack-models.py, with convert_lib.py (the ONNX to OpenVINO / INT8 converter it calls, the same file CamStack's Model Studio runs) and requirements.txt. It holds one recipe per hosted file: the upstream checkpoint, the input size, the Ultralytics export() arguments, the OpenVINO precision and the INT8 calibration count that file records about itself. Every hosted Ultralytics-derived file is rebuilt by one invocation: build-camstack-models.py --variant <file stem> (for example camstack-yolo26n-320 or camstack-yolov9m-640), --family <folder> or --all; --list prints the recipes and --check-hosted confirms against the mirror that every hosted file is a recipe output. The INT8 builds are NNCF post-training quantisation calibrated on COCO images: 128 for YOLO26, 300 for YOLOv9, as each IR records; the documented set is the first N of COCO val2017 in file-name order. The Python environment, the recorded tool versions and the calibration set are in source/README.md and the script's header. These scripts are CamStack's own code and are offered under the MIT licence; the weights they produce keep their upstream licence.

Limits of a rebuild:

• Tool versions. With the Ultralytics, PyTorch, coremltools and OpenVINO versions each recipe names, a rebuild reproduces the hosted weights. Other versions give the same weights in a possibly different graph.
• INT8 calibration. An IR records how many images calibrated it, not which ones. Calibrating on the same COCO images gives the documented build; a different set gives an equivalent, not bit-identical, INT8 model.

Non-commercial models

These are downloaded by default or selectable, and their terms do not allow commercial use:

Model	Used for	Terms
vehicle-type-v1 (default)	Vehicle classifier	CC-BY-NC-4.0; training data: CC-BY-NC-4.0 — treated as binding the trained weights (conservative reading) (terms)

Models

"Mirror" means https://huggingface.co/camstack/camstack-models, folder given. "Code / weights" gives the two licences separately because they often differ. Unknown means no licence could be traced; it does not mean free to use. default marks the model a step runs when nobody chose one; hidden marks an entry kept resolvable for a saved selection but no longer offered.

Object detection

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
yolo26n (default), yolo26s, yolo26m, yolo26l, yolo26x (hidden), yolo26n-fp16 (hidden), yolo26n-int8, yolo26s-fp16 (hidden), yolo26s-int8, yolo26m-fp16 (hidden), yolo26m-int8, yolo26l-fp16 (hidden), yolo26l-int8, yolo26n-320, yolo26n-320-int8, yolo26n-256, yolo26n-256-int8, yolo26s-320, yolo26s-320-int8, yolo26s-256, yolo26s-256-int8, yolo26m-320, yolo26m-320-int8, yolo26m-256, yolo26m-256-int8, yolo26l-320, yolo26l-320-int8, yolo26l-256, yolo26l-256-int8, yolo26x-fp16 (hidden), yolo26x-int8 (hidden)	Ultralytics	AGPL-3.0 / AGPL-3.0 (AGPL-3.0.txt)	COCO 2017: annotations CC-BY-4.0; images under their Flickr owners' terms	exported to ONNX, OpenVINO IR and CoreML (scripts/build-camstack-models.py); OpenVINO FP16 export; exported to OpenVINO IR, then OpenVINO NNCF INT8 post-training quantisation; re-exported at 320×320 to ONNX, OpenVINO IR and CoreML; re-exported at 320×320 to OpenVINO IR; OpenVINO NNCF INT8 post-training quantisation; re-exported at 256×256 to ONNX, OpenVINO IR and CoreML; re-exported at 256×256 to OpenVINO IR; OpenVINO NNCF INT8 post-training quantisation	mirror objectDetection/yolo26
ssd-mobilenet-v2-coco-edgetpu (default)	Google Coral test_data	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	COCO 2017: annotations CC-BY-4.0; images under their Flickr owners' terms	none	https://github.com/google-coral/test_data
yolov9t-320, yolov9t-320-int8, yolov9t-640, yolov9t-640-int8, yolov9s-320, yolov9s-320-int8, yolov9s-640, yolov9s-640-int8, yolov9m-320, yolov9m-320-int8, yolov9m-640, yolov9m-640-int8, yolov9c-320, yolov9c-320-int8, yolov9c-640, yolov9c-640-int8	Ultralytics	AGPL-3.0 / AGPL-3.0 (AGPL-3.0.txt)	COCO 2017: annotations CC-BY-4.0; images under their Flickr owners' terms	exported at 320×320 to ONNX, OpenVINO IR FP16 and CoreML; exported at 320×320 to OpenVINO IR; OpenVINO NNCF INT8 post-training quantisation; exported at 640×640 to ONNX, OpenVINO IR FP16 and CoreML; exported at 640×640 to OpenVINO IR; OpenVINO NNCF INT8 post-training quantisation	mirror objectDetection/yolov9

Package detection

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
rfdetr-package (default)	Roboflow RF-DETR	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	parcel fine-tuning set (not recorded): UNVERIFIED	RF-DETR-M fine-tuned to 2 classes at 576; exported to ONNX, OpenVINO IR and CoreML; training checkpoint .pth also published	mirror packageDetection/rfdetr-package

Face detection

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
yunet-2023mar (default)	OpenCV Zoo YuNet	BSD-3-Clause (BSD-3-Clause.txt) / MIT (MIT-yunet-reference.txt)	WIDER FACE: research-oriented terms; images CC BY-NC-ND	input contract baked into the graph (the pool RGB /255 becomes the upstream BGR 0..255: Mul(255) plus a channel Gather); internal tensor names cleared in the OpenVINO IR; ONNX fp32, OpenVINO IR fp32, CoreML fp16 (scripts/build-yunet-model.py)	mirror faceDetection/yunet

Face recognition

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
auraface-r100 (default)	fal.ai AuraFace v1 (glintr100.onnx only)	— / Apache-2.0 (Apache-2.0.txt)	—	input scaling y = 2x - 1 baked ahead of the first Conv (scripts/build-auraface-model.py); converted to OpenVINO IR and CoreML FP16	mirror faceRecognition/auraface

Licence plate detection

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
yolox-tiny-plate (default)	open-lpr models (faisalthaheem/open-lpr-models), YOLOX-tiny	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	Simanno annotated plate corpus plus community-contributed photographs, pseudo-labelled by a teacher model (not published; the upstream NOTICE says the photographs were uploaded without consent obtained): UNKNOWN	input contract baked into the graph (static 1x3x640x640 RGB /255 becomes the upstream BGR 0..255: Mul(255) plus a channel Gather) and the output reshaped to the YOLOv8 layout (cx, cy, w, h, obj*cls); ONNX fp32, OpenVINO IR fp16, CoreML fp16 (scripts/build-yolox-plate-model.py)	mirror plateDetection/yolox-tiny-plate

Licence plate reading

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
cct-s-v2-global (default)	ankandrew/fast-plate-ocr	MIT / MIT (MIT-fast-plate-ocr.txt)	fast-plate-ocr training set: private, not released upstream	input head removed (recorded in the ONNX doc_string); converted to OpenVINO IR and CoreML	mirror plateRecognition/cct-s-v2-global

Animal classifier

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
animal-classifier (default)	timm EfficientNet-Lite0, fine-tuned by CamStack	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	Open Images V7: annotations CC-BY-4.0 (Google LLC); images listed as CC-BY-2.0, no warranty	fine-tuned by CamStack on the Open Images V7 animal subset (8 classes); exported to ONNX, OpenVINO IR and CoreML	mirror animalClassification/animal-classifier

Bird classifier

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
bird-classifier (default)	Google AIY Vision birds_V1	— / Apache-2.0 (Apache-2.0.txt)	—	converted from TensorFlow with tf2onnx; OpenVINO IR and CoreML derived from it	mirror animalClassification/bird-classifier

Vehicle classifier

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
vehicle-type-v1 (default)	torchvision MobileNetV3-Large, fine-tuned by CamStack	BSD-3-Clause (BSD-3-Clause.txt) / CC-BY-NC-4.0 (CC-BY-NC-4.0.txt)	DrBimmer/vehicle-classification: CC-BY-NC-4.0 — treated as binding the trained weights (conservative reading)	fine-tuned by CamStack (8 classes); exported to ONNX, OpenVINO IR and CoreML	mirror vehicleClassification/vehicle-type-v1

Semantic search, image encoders (CLIP)

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
siglip2-b16-224 (default)	Google SigLIP 2 (google/siglip2-base-patch16-224)	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	WebLI: Google internal, not released	input scaling y = 2x - 1 baked ahead of the patch embedding; converted to OpenVINO IR and CoreML FP16, an FP32 ONNX hosted as the verification reference (scripts/build-siglip2-model.py)	mirror clip/siglip2

Semantic search, text encoders (CLIP)

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
siglip2-b16-224-text	Google SigLIP 2 (google/siglip2-base-patch16-224)	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	WebLI: Google internal, not released	input sliced to the model's 64 positions in the graph; ONNX weights FP16, OpenVINO IR and CoreML FP16; a Lowercase normalizer prepended to the Gemma tokenizer (scripts/build-siglip2-model.py)	mirror clip/siglip2

Segmentation

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
u2netp (default)	xuebinqin/U-2-Net	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	DUTS-TR: not checked	converted to ONNX, OpenVINO IR and CoreML	mirror segmentationRefiner/u2netp

Instance segmentation (dormant step)

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
yolo26n-seg, yolo26s-seg, yolo26m-seg	Ultralytics	AGPL-3.0 / AGPL-3.0 (AGPL-3.0.txt)	COCO 2017: annotations CC-BY-4.0; images under their Flickr owners' terms	exported to ONNX, OpenVINO IR and CoreML	mirror segmentation/yolo26-seg

Audio

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
yamnet-onnx (default)	tensorflow/models research/audioset/yamnet	Apache-2.0 / Apache-2.0 (Apache-2.0.txt)	AudioSet ontology/labels: CC-BY-4.0 (Google LLC)	converted from TensorFlow with tf2onnx; OpenVINO IR derived from it	mirror audioClassification/yamnet
apple-soundanalysis (default)	Apple SoundAnalysis (macOS built-in)	— / LicenseRef-Apple-macOS	—	none	built in, nothing downloaded

Language and vision-language models (AI addon)

Catalog ids	Upstream	Code / weights	Training data	CamStack changes	Downloaded from
llm-qwen2.5-1.5b-instruct-q4	Qwen (Alibaba Cloud)	— / Apache-2.0 (Apache-2.0.txt)	—	none (GGUF Q4_K_M published by Qwen; not re-hosted by CamStack)	https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF
llm-llama3.2-3b-instruct-q4	Meta Llama 3.2 (GGUF by bartowski)	— / LicenseRef-Llama-3.2-Community (Llama-3.2-Community.txt)	—	Q4_K_M GGUF quantisation by bartowski (not re-hosted by CamStack)	https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF
llm-qwen3-vl-2b-instruct-q4	Qwen3-VL (GGUF by unsloth)	— / Apache-2.0 (Apache-2.0.txt)	—	Q4_K_M GGUF quantisation by unsloth (not re-hosted by CamStack)	https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-GGUF
llm-qwen3-vl-4b-instruct-q4	Qwen3-VL (GGUF by unsloth)	— / Apache-2.0 (Apache-2.0.txt)	—	Q4_K_M GGUF quantisation by unsloth (not re-hosted by CamStack)	https://huggingface.co/unsloth/Qwen3-VL-4B-Instruct-GGUF
llm-qwen3-vl-8b-instruct-q4	Qwen3-VL (GGUF by unsloth)	— / Apache-2.0 (Apache-2.0.txt)	—	Q4_K_M GGUF quantisation by unsloth (not re-hosted by CamStack)	https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF
llm-qwen3.6-35b-a3b-ud-q4	Qwen3.6-35B-A3B (GGUF by unsloth)	— / Apache-2.0 (Apache-2.0.txt)	—	UD-Q4_K_M GGUF quantisation by unsloth (not re-hosted by CamStack)	https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF

Your own models

Model Studio can import third-party models (for example community object-detection catalogs) on request. They are downloaded from their publishers, are not hosted or redistributed by CamStack, and keep their publishers' licences; Model Studio shows the licence where the source declares one. A model you upload or convert yourself, or pull from your own Frigate+ account, likewise keeps whatever licence its author published. CamStack does not change it and does not record it.