Instructions to use NexusDwin/sailswarm-groundingdino-labeler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NexusDwin/sailswarm-groundingdino-labeler with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NexusDwin/sailswarm-groundingdino-labeler", dtype="auto") - Notebooks
- Google Colab
- Kaggle
SailSwarm GroundingDINO labeller (config / recipe β no weights)
This repo ships a configuration, not new weights. It is the tuned open-vocabulary detection recipe used as the labelling oracle for the SailSwarm obstacle-detection module β it pre-fills bounding-box suggestions that are human-audited and then distilled into the deployable YOLOv8n.
The model itself is the unmodified IDEA-Research/grounding-dino-base;
we only tuned the prompts, thresholds, and a multi-pass merge.
Recipe (detector_labeler.yaml)
Multi-pass, because GroundingDINO degrades with long prompts β each pass has a short focused phrase list and its own thresholds, merged with per-class NMS:
- objects (
box=0.30): boat / duck / buoy / person on the water. - structure (
box=0.20): poles / piling / ladder / platform. - structures_large (
box=0.20): crane / gantry / machinery. image_shortest_edge: 1000(recovers thin poles + small distant objects);boat_gate: 0.8(drops mast/rig boxes inside a boat to lift structure precision).
Result
Class-agnostic F1 0.701 (P 0.76, R 0.65) on the held-out Konstanz eval clip β the strongest box source we have; the distilled YOLOv8n reaches F1 0.673 and is the Pi-deployable detector.
Use
python -m scripts.eval.detector_labeler (SailSwarm repo) with this config.
License
GroundingDINO base model: Apache-2.0 (IDEA-Research). This config: same.
Model tree for NexusDwin/sailswarm-groundingdino-labeler
Base model
IDEA-Research/grounding-dino-base