YOLO26-ONNX

unileon-robotics/YOLO26-ONNX

YOLO26-ONNX is a object detection model from unileon-robotics. Use it when you need objects located in an image. It is set up for ultralytics. The card lists the license as agpl-3.0.

ONNX exports of the Ultralytics/YOLO26 checkpoints, one file per model size (n/s/m/l/x) and task (detection, instance segmentation, pose estimation, oriented bounding box, image classification).

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Updated Oct 15, 2026

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9.3 GB

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Hugging Face

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.onnx4.9 GB · 100%

At a glance

Task: Object Detection
Library: ultralytics
License: agpl-3.0
Access: Public
Created: Sep 21, 2026
Updated: Oct 15, 2026
SHA: e046fc51

Base models

Ultralytics/YOLO26

Model card

From the Hugging Face model README

YOLO26 ONNX

ONNX exports of the Ultralytics/YOLO26 checkpoints, one file per model size (n/s/m/l/x) and task (detection, instance segmentation, pose estimation, oriented bounding box, image classification).

All 25 files were exported with the Ultralytics exporter at imgsz=(480, 640), opset=12, FP32 with static input shapes, using the exporter defaults. The same 25 models are also shipped with fully dynamic batch, height and width axes under dynamic/, for batched/multi-camera consumers, and as compiled Qualcomm QNN context-binary models under qnn/, targeting the Hexagon NPU (HTP v75). Outputs follow the standard Ultralytics raw layouts listed below (classification outputs are already softmaxed probabilities). Class names are embedded in the ONNX graph metadata. The weights are unchanged from the earlier 640x640 exports - only the input geometry differs.

These files are derived from the Ultralytics YOLO26 checkpoints and inherit their license: AGPL-3.0. See https://www.ultralytics.com/license for the terms, and https://huggingface.co/Ultralytics/YOLO26 for the upstream weights.

Files

File	Task	Size	Input	Output	Classes	File size
`yolo26n-cls.onnx`	image classification	n	`images` 1×3×480×640	`output0` 1×1000 float	1000	11.3 MB
`yolo26s-cls.onnx`	image classification	s	`images` 1×3×480×640	`output0` 1×1000 float	1000	26.9 MB
`yolo26m-cls.onnx`	image classification	m	`images` 1×3×480×640	`output0` 1×1000 float	1000	46.6 MB
`yolo26l-cls.onnx`	image classification	l	`images` 1×3×480×640	`output0` 1×1000 float	1000	56.5 MB
`yolo26x-cls.onnx`	image classification	x	`images` 1×3×480×640	`output0` 1×1000 float	1000	118.6 MB
`yolo26n.onnx`	detection	n	`images` 1×3×480×640	`output0` 1×84×6300 float	80	9.9 MB
`yolo26s.onnx`	detection	s	`images` 1×3×480×640	`output0` 1×84×6300 float	80	38.2 MB
`yolo26m.onnx`	detection	m	`images` 1×3×480×640	`output0` 1×84×6300 float	80	81.9 MB
`yolo26l.onnx`	detection	l	`images` 1×3×480×640	`output0` 1×84×6300 float	80	99.6 MB
`yolo26x.onnx`	detection	x	`images` 1×3×480×640	`output0` 1×84×6300 float	80	223.2 MB
`yolo26n-obb.onnx`	oriented bounding box	n	`images` 1×3×480×640	`output0` 1×20×6300 float	15	10.0 MB
`yolo26s-obb.onnx`	oriented bounding box	s	`images` 1×3×480×640	`output0` 1×20×6300 float	15	39.2 MB
`yolo26m-obb.onnx`	oriented bounding box	m	`images` 1×3×480×640	`output0` 1×20×6300 float	15	85.1 MB
`yolo26l-obb.onnx`	oriented bounding box	l	`images` 1×3×480×640	`output0` 1×20×6300 float	15	102.7 MB
`yolo26x-obb.onnx`	oriented bounding box	x	`images` 1×3×480×640	`output0` 1×20×6300 float	15	230.5 MB
`yolo26n-pose.onnx`	pose estimation	n	`images` 1×3×480×640	`output0` 1×56×6300 float	1	12.0 MB
`yolo26s-pose.onnx`	pose estimation	s	`images` 1×3×480×640	`output0` 1×56×6300 float	1	41.8 MB
`yolo26m-pose.onnx`	pose estimation	m	`images` 1×3×480×640	`output0` 1×56×6300 float	1	86.5 MB
`yolo26l-pose.onnx`	pose estimation	l	`images` 1×3×480×640	`output0` 1×56×6300 float	1	104.2 MB
`yolo26x-pose.onnx`	pose estimation	x	`images` 1×3×480×640	`output0` 1×56×6300 float	1	230.6 MB
`yolo26n-seg.onnx`	instance segmentation	n	`images` 1×3×480×640	`output0` 1×116×6300 float<br>`output1` 1×32×120×160 float	80	11.2 MB
`yolo26s-seg.onnx`	instance segmentation	s	`images` 1×3×480×640	`output0` 1×116×6300 float<br>`output1` 1×32×120×160 float	80	41.9 MB
`yolo26m-seg.onnx`	instance segmentation	m	`images` 1×3×480×640	`output0` 1×116×6300 float<br>`output1` 1×32×120×160 float	80	94.6 MB
`yolo26l-seg.onnx`	instance segmentation	l	`images` 1×3×480×640	`output0` 1×116×6300 float<br>`output1` 1×32×120×160 float	80	112.2 MB
`yolo26x-seg.onnx`	instance segmentation	x	`images` 1×3×480×640	`output0` 1×116×6300 float<br>`output1` 1×32×120×160 float	80	251.6 MB

Dynamic exports

The same 25 models also ship under dynamic/ with the batch, height and width axes all dynamic, for batched consumers (e.g. the multi-camera yolo_batch_node in mgonz13/yolo_ros). They are the same weights re-exported with dynamic=True: the input is ['batch', 3, 'height', 'width'], so any batch size and any input resolution that is a multiple of 32 is accepted. Class names are embedded in the metadata as before.

File	Task	Size	Input	Output	Classes	File size
`dynamic/yolo26n-cls.onnx`	image classification	n	`images` batch×3×height×width	`output0` batch×1000 float	1000	11.3 MB
`dynamic/yolo26s-cls.onnx`	image classification	s	`images` batch×3×height×width	`output0` batch×1000 float	1000	27.0 MB
`dynamic/yolo26m-cls.onnx`	image classification	m	`images` batch×3×height×width	`output0` batch×1000 float	1000	46.6 MB
`dynamic/yolo26l-cls.onnx`	image classification	l	`images` batch×3×height×width	`output0` batch×1000 float	1000	56.6 MB
`dynamic/yolo26x-cls.onnx`	image classification	x	`images` batch×3×height×width	`output0` batch×1000 float	1000	118.7 MB
`dynamic/yolo26n.onnx`	detection	n	`images` batch×3×height×width	`output0` batch×84×anchors float	80	10.4 MB
`dynamic/yolo26s.onnx`	detection	s	`images` batch×3×height×width	`output0` batch×84×anchors float	80	38.6 MB
`dynamic/yolo26m.onnx`	detection	m	`images` batch×3×height×width	`output0` batch×84×anchors float	80	82.3 MB
`dynamic/yolo26l.onnx`	detection	l	`images` batch×3×height×width	`output0` batch×84×anchors float	80	100.8 MB
`dynamic/yolo26x.onnx`	detection	x	`images` batch×3×height×width	`output0` batch×84×anchors float	80	223.6 MB
`dynamic/yolo26n-obb.onnx`	oriented bounding box	n	`images` batch×3×height×width	`output0` batch×20×anchors float	15	10.9 MB
`dynamic/yolo26s-obb.onnx`	oriented bounding box	s	`images` batch×3×height×width	`output0` batch×20×anchors float	15	39.6 MB
`dynamic/yolo26m-obb.onnx`	oriented bounding box	m	`images` batch×3×height×width	`output0` batch×20×anchors float	15	85.6 MB
`dynamic/yolo26l-obb.onnx`	oriented bounding box	l	`images` batch×3×height×width	`output0` batch×20×anchors float	15	103.2 MB
`dynamic/yolo26x-obb.onnx`	oriented bounding box	x	`images` batch×3×height×width	`output0` batch×20×anchors float	15	232.3 MB
`dynamic/yolo26n-pose.onnx`	pose estimation	n	`images` batch×3×height×width	`output0` batch×56×anchors float	1	12.1 MB
`dynamic/yolo26s-pose.onnx`	pose estimation	s	`images` batch×3×height×width	`output0` batch×56×anchors float	1	41.9 MB
`dynamic/yolo26m-pose.onnx`	pose estimation	m	`images` batch×3×height×width	`output0` batch×56×anchors float	1	86.7 MB
`dynamic/yolo26l-pose.onnx`	pose estimation	l	`images` batch×3×height×width	`output0` batch×56×anchors float	1	104.5 MB
`dynamic/yolo26x-pose.onnx`	pose estimation	x	`images` batch×3×height×width	`output0` batch×56×anchors float	1	231.3 MB
`dynamic/yolo26n-seg.onnx`	instance segmentation	n	`images` batch×3×height×width	`output0` batch×116×anchors float<br>`output1` batch×32×mask_height×mask_width float	80	11.5 MB
`dynamic/yolo26s-seg.onnx`	instance segmentation	s	`images` batch×3×height×width	`output0` batch×116×anchors float<br>`output1` batch×32×mask_height×mask_width float	80	42.2 MB
`dynamic/yolo26m-seg.onnx`	instance segmentation	m	`images` batch×3×height×width	`output0` batch×116×anchors float<br>`output1` batch×32×mask_height×mask_width float	80	95.0 MB
`dynamic/yolo26l-seg.onnx`	instance segmentation	l	`images` batch×3×height×width	`output0` batch×116×anchors float<br>`output1` batch×32×mask_height×mask_width float	80	114.1 MB
`dynamic/yolo26x-seg.onnx`	instance segmentation	x	`images` batch×3×height×width	`output0` batch×116×anchors float<br>`output1` batch×32×mask_height×mask_width float	80	252.1 MB

QNN exports

The same 25 checkpoints are also available as Qualcomm QNN models under qnn/. Each file is an ONNX Runtime EPContext model with the compiled QNN context binary embedded (no external context files), produced with the Ultralytics format=qnn exporter. The graph is quantized to W8A16 (8-bit weights, 16-bit activations) through ONNX Runtime's QNN QDQ flow, calibrated on the Ultralytics sample datasets, and finalized for the Hexagon Tensor Processor (HTP) v75 target (Snapdragon 8 Gen 3-class NPUs; the embedded context reports soc_type=QCS8300).

Inputs are static and channel-last (NHWC): images 1×640×480×3, and 1×640×640×3 for classification - a different geometry from the channel-first 1×3×480×640 FP32 exports above. Outputs follow the same raw layouts as the FP32 exports. These models only run through ONNX Runtime's QNN Execution Provider on Snapdragon hardware; the QNN context cannot run on the CPU or CUDA providers.

File	Task	Size	Input	Output	Classes	File size
`qnn/yolo26n-cls_qnn.onnx`	image classification	n	`images` 1×640×640×3	`output0` 1×1000 float	1000	3.3 MB
`qnn/yolo26s-cls_qnn.onnx`	image classification	s	`images` 1×640×640×3	`output0` 1×1000 float	1000	7.4 MB
`qnn/yolo26m-cls_qnn.onnx`	image classification	m	`images` 1×640×640×3	`output0` 1×1000 float	1000	12.7 MB
`qnn/yolo26l-cls_qnn.onnx`	image classification	l	`images` 1×640×640×3	`output0` 1×1000 float	1000	15.5 MB
`qnn/yolo26x-cls_qnn.onnx`	image classification	x	`images` 1×640×640×3	`output0` 1×1000 float	1000	31.9 MB
`qnn/yolo26n_qnn.onnx`	detection	n	`images` 1×640×480×3	`output0` 1×84×6300 float	80	3.4 MB
`qnn/yolo26s_qnn.onnx`	detection	s	`images` 1×640×480×3	`output0` 1×84×6300 float	80	10.7 MB
`qnn/yolo26m_qnn.onnx`	detection	m	`images` 1×640×480×3	`output0` 1×84×6300 float	80	22.1 MB
`qnn/yolo26l_qnn.onnx`	detection	l	`images` 1×640×480×3	`output0` 1×84×6300 float	80	27.0 MB
`qnn/yolo26x_qnn.onnx`	detection	x	`images` 1×640×480×3	`output0` 1×84×6300 float	80	59.2 MB
`qnn/yolo26n-obb_qnn.onnx`	oriented bounding box	n	`images` 1×640×480×3	`output0` 1×20×6300 float	15	3.5 MB
`qnn/yolo26s-obb_qnn.onnx`	oriented bounding box	s	`images` 1×640×480×3	`output0` 1×20×6300 float	15	11.0 MB
`qnn/yolo26m-obb_qnn.onnx`	oriented bounding box	m	`images` 1×640×480×3	`output0` 1×20×6300 float	15	22.9 MB
`qnn/yolo26l-obb_qnn.onnx`	oriented bounding box	l	`images` 1×640×480×3	`output0` 1×20×6300 float	15	27.8 MB
`qnn/yolo26x-obb_qnn.onnx`	oriented bounding box	x	`images` 1×640×480×3	`output0` 1×20×6300 float	15	61.0 MB
`qnn/yolo26n-pose_qnn.onnx`	pose estimation	n	`images` 1×640×480×3	`output0` 1×56×6300 float	1	4.0 MB
`qnn/yolo26s-pose_qnn.onnx`	pose estimation	s	`images` 1×640×480×3	`output0` 1×56×6300 float	1	11.8 MB
`qnn/yolo26m-pose_qnn.onnx`	pose estimation	m	`images` 1×640×480×3	`output0` 1×56×6300 float	1	23.5 MB
`qnn/yolo26l-pose_qnn.onnx`	pose estimation	l	`images` 1×640×480×3	`output0` 1×56×6300 float	1	28.4 MB
`qnn/yolo26x-pose_qnn.onnx`	pose estimation	x	`images` 1×640×480×3	`output0` 1×56×6300 float	1	61.1 MB
`qnn/yolo26n-seg_qnn.onnx`	instance segmentation	n	`images` 1×640×480×3	`output0` 1×116×6300 float<br>`output1` 1×32×160×120 float	80	3.8 MB
`qnn/yolo26s-seg_qnn.onnx`	instance segmentation	s	`images` 1×640×480×3	`output0` 1×116×6300 float<br>`output1` 1×32×160×120 float	80	11.7 MB
`qnn/yolo26m-seg_qnn.onnx`	instance segmentation	m	`images` 1×640×480×3	`output0` 1×116×6300 float<br>`output1` 1×32×160×120 float	80	25.5 MB
`qnn/yolo26l-seg_qnn.onnx`	instance segmentation	l	`images` 1×640×480×3	`output0` 1×116×6300 float<br>`output1` 1×32×160×120 float	80	30.4 MB
`qnn/yolo26x-seg_qnn.onnx`	instance segmentation	x	`images` 1×640×480×3	`output0` 1×116×6300 float<br>`output1` 1×32×160×120 float	80	66.6 MB

Export recipe

uv run --with ultralytics --with onnx --with onnxruntime --with onnxslim \
  yolo export model=yolo26m.pt format=onnx imgsz=480,640 opset=12

Add dynamic=True for the dynamic/ variant: batch, height and width all stay dynamic, which is what the dynamic/ files ship (no post-export pinning step).

For the qnn/ variant use format=qnn name=75 instead - the model is quantized to W8A16 and compiled to an embedded QNN context binary for the HTP v75 target (no Snapdragon device is needed for the export step):

uv run --with ultralytics --with onnxruntime-qnn \
  yolo export model=yolo26m.pt format=qnn name=75 imgsz=640,480

Use imgsz=640 for classification. onnxruntime-qnn bundles the Qualcomm AI Runtime (QAIRT) libraries and is installed automatically on first QNN export.

The .pt checkpoint names follow yolo26{size}{-task} (no task suffix for detection; -seg, -pose, -obb, -cls for the other tasks); the exported .onnx keeps the same stem. Replace yolo26m.pt with the checkpoint of interest.

Task: Object Detection
Library: ultralytics
License: agpl-3.0
Created: Sep 21, 2026
Updated: Oct 15, 2026