| ---
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| license: cc-by-sa-4.0
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| pipeline_tag: object-detection
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| datasets:
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| - PINGEcosystem/sss-crab-pot-detection-ds
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| language:
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| - en
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| metrics:
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| - accuracy
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| tags:
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| - side-scan-sonar
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| - sonar
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| - crab-pot
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| - object-detection
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| - yolo
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| - onnx
|
| ---
|
|
|
| # 🦀 Ghost Pot YOLO12
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| **YOLO12 model for side-scan sonar ghost pot detection**
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|
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| [](https://huggingface.co/PINGEcosystem/gv-yolo12)
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| [](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds)
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|
|
| This repository contains a fine-tuned YOLO-based object-detection model for identifying derelict crab pots in side-scan sonar imagery. The model was trained on the [PINGEcosystem/sss-crab-pot-detection-ds](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds) dataset, which contains annotated sonar imagery collected in Delaware's Inland Bays and Delaware Bay.
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| The model is part of the [GhostVision](https://github.com/PINGEcosystem/GhostVision) effort to support scalable detection and mapping of derelict fishing gear from acoustic imagery.
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|
|
| ## 📜 Publication
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| *In Progress*
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|
|
| ## 🧠 Model Overview
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| - Architecture: YOLO12 Small (`yolov12s`)
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| - Task: Object detection
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| - Input modality: Side-scan sonar imagery
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| - Primary target class: Crab-Pot
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| - Training dataset: [PINGEcosystem/sss-crab-pot-detection-ds](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds)
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| This model was trained from a dataset that originally contained both `Crab-Pot` and `Maybe-Crab-Pot` labels. During preprocessing, ambiguous `Maybe-Pot` examples were omitted so the exported detector predicts a single foreground class:
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| - `Crab-Pot`
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|
|
| ## 📦 Files
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| - `model.safetensors` - serialized model weights
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| - `weights.onnx` - ONNX export for portable inference
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| - `class_names.txt` - class label mapping used for export
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| - `model_type.json` - model/task metadata
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| - `environment.json` - training/export environment metadata
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| - `README.md` - model card and usage guidance
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|
|
| ## 🗂️ Training Data
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| This model was trained using the [PINGEcosystem/sss-crab-pot-detection-ds](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds) dataset.
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| Dataset highlights:
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| - 6,674 annotated sonar images
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| - Consumer-grade Humminbird side-scan sonar imagery
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| - Bounding-box annotations in JSONL format
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| - Data collected from northern Rehoboth Bay and Indian River Bay, Delaware
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|
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| Export metadata highlights:
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| - Model variant: `yolov12s`
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| - Input resolution: `640`
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| - Exported label space: `Crab-Pot`
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| - Dataset endpoint version: `rx5YMJ3d3GZMFobFJf3Y/5`
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| If you are looking for the source annotations, data splits, and schema details, use the dataset card above.
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|
|
| ## 🎯 Intended Use
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| This model is intended for:
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| - automated detection of derelict crab pots in side-scan sonar imagery
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| - research workflows for marine debris mapping
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| - benchmarking sonar object-detection pipelines
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| - downstream human-in-the-loop review and prioritization
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| This model is not intended to replace field validation or expert review in operational removal workflows.
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|
|
| ## 🧪 Inference Notes
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| The repository includes native weights exported to `model.safetensors` and an ONNX export in `weights.onnx` to support portable inference workflows.
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| The exported label space is:
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|
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| ```text
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| Crab-Pot
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| ```
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| Predictions should be interpreted in the context of sonar-specific variability such as substrate texture, tow geometry, acoustic shadowing, and target burial.
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|
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| ## 🚀 Usage
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|
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| ### 📡 Use with GhostVision
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| For end-to-end processing of side-scan sonar data, use this model through [GhostVision](https://github.com/PINGEcosystem/GhostVision), which handles sonar preprocessing, moving-window tiling, inference orchestration, optional tracking, and georeferencing.
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|
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| Typical GhostVision workflow:
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| 1. Install and launch GhostVision.
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| 2. Open your sonar recording or batch folder.
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| 3. Select the YOLO12 model in the model dropdown.
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| 4. Run detection and review the exported detections, shapefiles, and waypoint products.
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| Within the current GhostVision codebase, this model is exposed through the known model alias `yolo12_v1` and resolved to the packaged model directory `ghostvision-models/5`.
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|
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| ### 💻 Use without GhostVision
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| If you only want to run the detector itself, use the exported `weights.onnx` file directly with ONNX Runtime.
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|
|
| ```bash
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| pip install huggingface_hub onnxruntime pillow numpy
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| ```
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|
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| ```python
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| from huggingface_hub import hf_hub_download
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| import numpy as np
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| from PIL import Image
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| import onnxruntime as ort
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| repo_id = "PINGEcosystem/gv-yolo12"
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| model_path = hf_hub_download(repo_id=repo_id, filename="weights.onnx")
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| session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
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| input_name = session.get_inputs()[0].name
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| image = Image.open("path/to/sonar-image.jpg").convert("RGB").resize((512, 512))
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| image_np = np.asarray(image, dtype=np.float32) / 255.0
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| image_np = np.transpose(image_np, (2, 0, 1))[None, ...]
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|
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| raw_outputs = session.run(None, {input_name: image_np})
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| output_map = {
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| output_meta.name: output_value
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| for output_meta, output_value in zip(session.get_outputs(), raw_outputs)
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| }
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| for name, value in output_map.items():
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| print(name, value.shape)
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| ```
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| This direct ONNX path is useful when you want to embed the detector in another application or build your own post-processing pipeline. The exact output tensors depend on the export format, so inspect the returned names and shapes before writing box filtering and visualization code.
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|
|
| ## ⚠️ Limitations
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| - Performance may degrade on sonar systems, substrates, or regions that differ from the Delaware training domain.
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| - Small, partially buried, or weak-return targets may be missed.
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| - Sonar artifacts and hard-bottom features may produce false positives.
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| - The model predicts only the retained foreground class used during export.
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| - Outputs should be reviewed by domain experts before management or removal decisions are made.
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|
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| ## 🔗 Related Resources
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| - Dataset: [PINGEcosystem/sss-crab-pot-detection-ds](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds)
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| - GhostVision project: [PINGEcosystem/GhostVision](https://github.com/PINGEcosystem/GhostVision)
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| - RF-DETR companion model: [PINGEcosystem/gv-rf-detr](https://huggingface.co/PINGEcosystem/gv-rf-detr)
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| - YOLO26 companion model: [PINGEcosystem/gv-yolo26](https://huggingface.co/PINGEcosystem/gv-yolo26)
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|
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| ## 📜 License
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| This model card and associated artifacts are released under the license specified in this repository metadata.
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|
|
| ## 🙌 Acknowledgments
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| This work was developed with support from:
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| - University of Delaware -- Center for Coastal Sediments Hydrodynamics and Engineering Lab (CSHEL)
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| - Delaware Sea Grant
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| - 2024 Autonomous Systems Bootcamp
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| - NOAA's Project ABLE
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| - NOAA Marine Debris Program
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| - Delaware Department of Natural Resources and Environmental Control (DNREC)
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| - Community volunteers participating in ghost-gear surveys
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|
|
