SenseNova-Vision

SenseNova-Vision is a unified multimodal model that casts vision tasks as prompted generation on a shared decoder: boxes, points, keypoints and OCR words come out as tagged text, and depth, mask and panoptic maps come out as images a decoder renders. LibreYOLO loads it through LibreVLM and supports seven tasks from the one 7B checkpoint.

Tasks: detection, instance segmentation, panoptic, pose, point, depth, ocr
Sizes: 7b at 1024 px
Install: pip install libreyolo
Support tier: Sibling tier, since v. A separate product surface with its own factory and contract.
Upstream: SenseNova-Vision by SenseTime (OpenSenseNova), Apache-2.0 (code); CC BY-NC 4.0 (weights). Paper, source
Licenses: Code Apache-2.0, weights Apache-2.0 (code); CC BY-NC 4.0 (weights). Commercial use

Install

SenseNova-Vision needs its own extra, which pulls in accelerate for the big-model dispatch this checkpoint needs and, on non-macOS platforms, bitsandbytes for 4-bit loading.

bash

pip install "libreyolo[sensenova]"

The checkpoint is mirrored on Hugging Face under LibreYOLO's own org and downloads automatically on first use; it is CC BY-NC 4.0, non-commercial use only, and the loader prints that notice before every automatic download. See Licensing below.

Predict

Python

from libreyolo import LibreVLM model = LibreVLM("sensenova-vision", task="detect")model.set_classes(["bird", "boat"])result = model.predict("image.jpg")print(result.boxes.xyxy) # set_task() switches tasks on the same loaded model.model.set_task("depth")result = model.predict("image.jpg")depth = result.depth_map.data

Referring segmentation and panoptic

from libreyolo import LibreVLM model = LibreVLM("sensenova-vision", task="segment")# Segmentation is referring: it needs a target phrase, not a class list.model.set_classes(["the person furthest to the right"])result = model.predict("street.jpg")mask = result.masks.data[0] model.set_task("panoptic")# With no custom vocabulary, panoptic falls back to the COCO panoptic# categories the checkpoint was tuned on.result = model.predict("street.jpg")segment_map = result.panoptic.datafor segment in result.panoptic.segments_info:    print(segment)

Points, pose and OCR

from libreyolo import LibreVLM model = LibreVLM("sensenova-vision", task="point")model.set_classes(["screw"])result = model.predict("board.jpg")print(result.points.xy) # With no vocabulary set, pose falls back to "person".model.set_task("pose")result = model.predict("gym.jpg")print(result.boxes.xyxy, result.keypoints.data.shape) model.set_task("ocr")result = model.predict("sign.jpg")print(result.ocr.texts)

Every prediction is a diffusion decode over the shared Bagel-MoT backbone, so it is a capability model rather than a real-time one: expect noticeably higher per-image latency than a purpose-built detector or segmenter. dtype="auto" (the default) loads bf16 on a GPU with enough memory and falls back to 4-bit NF4 quantization elsewhere, which needs bitsandbytes; pass dtype="bf16" to force full precision on a large enough GPU. noise_seed=42 at construction seeds the diffusion sampler for reproducible dense outputs; pass noise_seed=None to disable seeding.

The seven tasks share one loaded checkpoint: set_task() switches between them without reloading. set_classes() sets the active vocabulary; detection, points, pose and panoptic accept a class list, while segmentation is referring and needs exactly the phrase to isolate. Each task returns the standard Results object with a different payload populated: boxes for detect, points for point, boxes and keypoints for pose, ocr for OCR, depth_map for depth, masks for segment, and panoptic (with segments_info) for panoptic. See prediction for sources, streaming and result handling.

Checkpoints

File	Input (px)	Weights license
Detection
SenseNovaVision7b.pt	1024	cc-by-nc-4.0

Every file above exists in the LibreYOLO org today and downloads on first use.

Licensing

Check the license on the Hugging Face repository of the specific weights you download. Every checkpoint in the LibreYOLO org carries one, and they are not always the same across a family. That repository is the authoritative source; the summary below describes what applied when this page was last verified.

This is a description of the licenses involved, not legal advice. If the answer matters commercially, read the licenses yourself and take your own counsel.

Original work: SenseNova-Vision, SenseTime (OpenSenseNova)
Upstream license: Apache-2.0 (code); CC BY-NC 4.0 (weights)
Upstream source: github.com/OpenSenseNova/SenseNova-Vision
LibreYOLO code: MIT
Weights: Apache-2.0 (code); CC BY-NC 4.0 (weights), republished at huggingface.co/LibreYOLO
Interpretation: LibreYOLO's SenseNova-Vision port is Apache-2.0 code adapted from SenseTime's OpenSenseNova/SenseNova-Vision release, which is itself built on Apache-2.0 sources: ByteDance's Bagel decoder, Hugging Face Transformers' Qwen2 and SigLIP modules, and Black Forest Labs' FLUX autoencoder. The SenseNova-Vision-7B-MoT checkpoint is a separate artifact under CC BY-NC 4.0, NON-COMMERCIAL USE ONLY. LibreYOLO mirrors it byte-identical, with attribution, at LibreYOLO/SenseNovaVision7b, but mirroring does not change the license: it stays non-commercial, and the loader prints that notice before every automatic download. One upstream file, modeling/bagel/modeling_utils.py, carries an incompatible CC BY-NC 4.0 license inherited from Meta's DiT; LibreYOLO did not port it. The small permissive routines it would have supplied were re-derived independently instead, from Hugging Face Transformers' ViTMAE implementation (Apache-2.0) and OpenAI's guided-diffusion (MIT).

Citation

@misc{sensenova2026sensenovavision,
      title={Vision as Unified Multimodal Generation}, 
      author={Xiaoyang Han and Jianhua Li and Kewang Deng and Zukai Chen and Xuanke Shi and Sihan Wang and Boxuan Li and Linyan Wang and Siyi Xie and Xin You and Jinsheng Quan and Zhongang Cai and Haiwen Diao and Ziwei Liu and Lei Yang and Dahua Lin and Quan Wang},
      year={2026},
      eprint={2607.06560},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.06560}, 
}

Copied from the authors' citation block at github.com/OpenSenseNova/SenseNova-Vision#citation.