LocateAnything

Install

LocateAnything needs the vlm extra, which pulls in transformers plus the decord, lmdb and peft packages its Hugging Face remote code imports at load time.

pip install "libreyolo[vlm]"

Predict

LibreLocateAnything is a Python class, not a .pt checkpoint: it is not loaded through the LibreYOLO() factory, and the libreyolo CLI does not resolve it. The LibreVLM(...) factory (from libreyolo import LibreVLM) also reaches this family by alias, e.g. LibreVLM("locate-anything"); the class used below is what it constructs. Loading it downloads and executes NVIDIA's own remote model code from Hugging Face, so LibreYOLO pins the download to one fixed commit revision rather than the mutable main branch, and logs a one-time license notice before the first download.

from libreyolo import LibreLocateAnything, SAMPLE_IMAGE model = LibreLocateAnything(size="3b") # Open vocabulary: any words work, not a fixed class head. Sticky# across every later predict()/track() call until set again.model.set_classes(["person", "bicycle", "dog"])result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

from libreyolo import LibreLocateAnything, SAMPLE_IMAGE # task="point" returns one point per matched object instead of a box.# Switch tasks on an already-loaded model with model.set_task("point").model = LibreLocateAnything(size="3b", task="point")model.set_classes(["the person closest to the camera"])result = model(SAMPLE_IMAGE, save=True) for pt in result.points:    print(pt.cls, pt.conf, pt.xy)

from libreyolo import LibreLocateAnything, SAMPLE_IMAGE model = LibreLocateAnything(size="3b") # The escape hatch beneath the detection convenience: free-form# questions, counting, or any prompt the boxes wrapper doesn't cover.text = model.chat(SAMPLE_IMAGE, "Describe the scene in one sentence.")print(text)

result.boxes (task detect) and result.points (task point) carry the parsed output like any other family. Confidence is a placeholder: LocateAnything emits no per-box score, so every detection gets the same constant confidence, and conf= only drops rows below that constant, it does not rank them. Skip set_classes() and the vocabulary defaults to the COCO-80 names. See prediction for sources, streaming and result handling.

Variants

One published size, 3b. Two tasks share the same weights: detect (the default) returns boxes, and task="point" returns a single point per matched object instead, in result.points; switch between them on an already-loaded model with model.set_task("point"). LibreYOLO's benchmark harness has not measured this family, so there are no published accuracy numbers to compare against.

LibreYOLO exposes this family for prediction only. train(), val() and export() all raise NotImplementedError: fine-tune upstream and load the result instead, dataset validation is skipped because a placeholder confidence would make COCO mAP misleading, and export is out of scope for a generative model with no state dict to trace.

Licensing

Citation

@article{wang2025locateanything,
  title   = {LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding},
  author  = {Shihao Wang and Shilong Liu and Yuanguo Kuang and Xinyu Wei and
             Yangzhou Liu and Zhiqi Li and Yunze Man and Guo Chen and
             Andrew Tao and Guilin Liu and Jan Kautz and Lei Zhang and Zhiding Yu},
  journal = {arXiv:2605.27365},
  year    = {2026},
}