Qwen3-VL

Install

Qwen3-VL belongs to LibreYOLO's VLM-as-detector tier, a separate product surface from the checkpoint-based families with its own factory. It needs the vlm extra.

pip install "libreyolo[vlm]"

Predict

Weights download from Hugging Face on first use and are cached locally. LibreVLM() called with no argument defaults to Qwen3-VL-4B.

from libreyolo import LibreVLM, SAMPLE_IMAGE model = LibreVLM("qwen3-vl-4b")model.set_classes(["forklift", "pallet", "safety vest"])result = model.predict(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

from libreyolo import LibreVLM, SAMPLE_IMAGE model = LibreVLM("qwen3-vl-4b") # The escape hatch beneath the detection convenience: any question,# not just a bounding-box query.answer = model.chat(SAMPLE_IMAGE, "How many people are wearing a safety vest?")print(answer)

This family loads through the LibreVLM() factory, not LibreYOLO(): VLM families declare no checkpoint loader, so the file-suffix routing described on other model pages does not apply here. set_classes() sets the vocabulary Qwen3-VL is asked to find; it is sticky, so it stays in effect across every later predict()/track() call until you set it again. Every detection carries the same placeholder confidence, so conf filtering is all-or-nothing rather than a ranking; iou does have an effect for this family, dropping a later same-class box once it overlaps an already-kept one past the threshold, since a repeating generator can otherwise emit near-duplicate boxes for one object. Unlike Florence-2 and Kosmos-2, Qwen3-VL also answers free-form questions through chat(), the same escape hatch documented on the LibreVLM factory. LibreYOLO's CLI does not cover this tier: there is no libreyolo predict model=... form for it. See prediction for sources, streaming and result handling.

Variants

Three sizes: Qwen3-VL-2B-Instruct, Qwen3-VL-4B-Instruct and Qwen3-VL-8B-Instruct, loaded as LibreVLM("qwen3-vl-2b"), LibreVLM("qwen3-vl-4b") and LibreVLM("qwen3-vl-8b"). All three declare a nominal 1024 px input, but the Qwen processor's own smart-resize decides the actual canvas passed to the network, so that figure is not a fixed operating resolution the way it is for the other families on this site. LibreYOLO has not published a benchmark comparing accuracy across the three sizes.

LibreYOLO does not train, validate or export Qwen3-VL: train(), val() and export() all raise NotImplementedError for every family in this tier (see the support tier above). Fine-tune Qwen3-VL upstream and load the resulting weights if you need a custom vocabulary baked in; check predict() output by eye instead of a COCO-style validation pass, since every detection carries the same placeholder confidence.

Licensing

Citation

@article{Qwen3-VL,
      title={Qwen3-VL Technical Report}, 
      author={Shuai Bai and Yuxuan Cai and Ruizhe Chen and Keqin Chen and Xionghui Chen and Zesen Cheng and Lianghao Deng and Wei Ding and Chang Gao and Chunjiang Ge and Wenbin Ge and Zhifang Guo and Qidong Huang and Jie Huang and Fei Huang and Binyuan Hui and Shutong Jiang and Zhaohai Li and Mingsheng Li and Mei Li and Kaixin Li and Zicheng Lin and Junyang Lin and Xuejing Liu and Jiawei Liu and Chenglong Liu and Yang Liu and Dayiheng Liu and Shixuan Liu and Dunjie Lu and Ruilin Luo and Chenxu Lv and Rui Men and Lingchen Meng and Xuancheng Ren and Xingzhang Ren and Sibo Song and Yuchong Sun and Jun Tang and Jianhong Tu and Jianqiang Wan and Peng Wang and Pengfei Wang and Qiuyue Wang and Yuxuan Wang and Tianbao Xie and Yiheng Xu and Haiyang Xu and Jin Xu and Zhibo Yang and Mingkun Yang and Jianxin Yang and An Yang and Bowen Yu and Fei Zhang and Hang Zhang and Xi Zhang and Bo Zheng and Humen Zhong and Jingren Zhou and Fan Zhou and Jing Zhou and Yuanzhi Zhu and Ke Zhu},
	  journal={arXiv preprint arXiv:2511.21631},
      year={2025}
}