---
license: cc-by-sa-4.0
pipeline_tag: object-detection
datasets:
- PINGEcosystem/sss-crab-pot-detection-ds
language:
- en
metrics:
- accuracy
tags:
- side-scan-sonar
- sonar
- crab-pot
- object-detection
- rfdetr
- onnx
---

# 🦀 Ghost Pot RF-DETR
**RF-DETR model for side-scan sonar ghost pot detection**

[![Model on Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-yellow)](https://huggingface.co/PINGEcosystem/gv-rf-detr)
[![Dataset on Hugging Face](https://img.shields.io/badge/Hugging%20Face-Dataset-orange)](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds)

This repository contains a fine-tuned RF-DETR 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.

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.

## 📜 Publication
*In Progress*

## 🧠 Model Overview
- Architecture: RF-DETR Small (`rfdetr-small`)
- Task: Object detection
- Input modality: Side-scan sonar imagery
- Primary target class: Crab-Pot
- Training dataset: [PINGEcosystem/sss-crab-pot-detection-ds](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds)

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:

- `Crab-Pot`

## 📦 Files
- `model.safetensors` - serialized model weights
- `weights.onnx` - ONNX export for portable inference
- `class_names.txt` - class label mapping used for export
- `model_type.json` - model/task metadata
- `environment.json` - training/export environment metadata
- `README.md` - model card and usage guidance

## 🗂️ Training Data
This model was trained using the [PINGEcosystem/sss-crab-pot-detection-ds](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds) dataset.

Dataset highlights:
- 6,674 annotated sonar images
- Consumer-grade Humminbird side-scan sonar imagery
- Bounding-box annotations in JSONL format
- Data collected from northern Rehoboth Bay and Indian River Bay, Delaware

Export metadata highlights:
- Model variant: `rfdetr-small`
- Input resolution: `512`
- Exported label space: `Crab-Pot`
- Dataset endpoint version: `rx5YMJ3d3GZMFobFJf3Y/1`

If you are looking for the source annotations, data splits, and schema details, use the dataset card above.

## 🎯 Intended Use
This model is intended for:
- automated detection of derelict crab pots in side-scan sonar imagery
- research workflows for marine debris mapping
- benchmarking sonar object-detection pipelines
- downstream human-in-the-loop review and prioritization

This model is not intended to replace field validation or expert review in operational removal workflows.

## 🧪 Inference Notes
The repository includes both native weights and an ONNX export to support different deployment targets.

The exported label space is:

```text
Crab-Pot
```

Predictions should be interpreted in the context of sonar-specific variability such as substrate texture, tow geometry, acoustic shadowing, and target burial.

## 🚀 Usage

### 📡 Use with GhostVision
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.

Typical GhostVision workflow:
1. Install and launch GhostVision.
2. Open your sonar recording or batch folder.
3. Select the RF-DETR model in the model dropdown.
4. Run detection and review the exported detections, shapefiles, and waypoint products.

If you are working from the sibling `GhostVision` repository in this workspace, the relevant inference flow is implemented around `ghostvision.detect`, `ghostvision.main_crabDetect`, and `ghostvision.class_crabObj_rf`.

### 💻 Use without GhostVision
If you only want to run the detector itself, use the exported `weights.onnx` file directly with ONNX Runtime.

```bash
pip install huggingface_hub onnxruntime pillow numpy
```

```python
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
import onnxruntime as ort

repo_id = "PINGEcosystem/gv-rf-detr"
model_path = hf_hub_download(repo_id=repo_id, filename="weights.onnx")

session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
input_name = session.get_inputs()[0].name

image = Image.open("path/to/sonar-image.jpg").convert("RGB").resize((512, 512))
image_np = np.asarray(image, dtype=np.float32) / 255.0
image_np = np.transpose(image_np, (2, 0, 1))[None, ...]

raw_outputs = session.run(None, {input_name: image_np})
output_map = {
	output_meta.name: output_value
	for output_meta, output_value in zip(session.get_outputs(), raw_outputs)
}

for name, value in output_map.items():
	print(name, value.shape)
```

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.

## ⚠️ Limitations
- Performance may degrade on sonar systems, substrates, or regions that differ from the Delaware training domain.
- Small, partially buried, or weak-return targets may be missed.
- Sonar artifacts and hard-bottom features may produce false positives.
- The model predicts only the retained foreground class used during export.
- Outputs should be reviewed by domain experts before management or removal decisions are made.

## 🔗 Related Resources
- Dataset: [PINGEcosystem/sss-crab-pot-detection-ds](https://huggingface.co/datasets/PINGEcosystem/sss-crab-pot-detection-ds)
- GhostVision project: [PINGEcosystem/GhostVision](https://github.com/PINGEcosystem/GhostVision)
- YOLO12 companion model: [PINGEcosystem/gv-yolo12](https://huggingface.co/PINGEcosystem/gv-yolo12)
- YOLO26 companion model: [PINGEcosystem/gv-yolo26](https://huggingface.co/PINGEcosystem/gv-yolo26)

## 📜 License
This model card and associated artifacts are released under the license specified in this repository metadata.

## 🙌 Acknowledgments
This work was developed with support from:
- University of Delaware -- Center for Coastal Sediments Hydrodynamics and Engineering Lab (CSHEL)
- Delaware Sea Grant
- 2024 Autonomous Systems Bootcamp
- NOAA's Project ABLE
- NOAA Marine Debris Program
- Delaware Department of Natural Resources and Environmental Control (DNREC)
- Community volunteers participating in ghost-gear surveys