Megalodon 2024 Yolov11
Trained by researchers at the Monterey Bay Aquarium Research Institute (MBARI). Ultralytics YOLO11x Object detection model Fine-tuned to detect 1 class, called 'object', using all FathomNet localizations Updated from...
Source du modèle
- Type
- Modèle
- Taille du fichier
- 109 MB
- Version
- vRevision 403704d
- Mis à jour
- 28 oct. 2026
- Téléchargements
- 52
- Source
- Hugging Face
Description de la source
FathomNet Megalodon Detector
Model Details
- Trained by researchers at the Monterey Bay Aquarium Research Institute (MBARI).
- Ultralytics YOLO11x
- Object detection model
- Fine-tuned to detect 1 class, called 'object', using all FathomNet localizations
- Updated from the
yolo11x_megalodon_e70_p10training run
Intended Use
- Post-process video and images collected by marine researchers
- Can be used to build a localized set of training images, when neither training data nor a model exists for the imagery being analyzed
Factors
- Distribution shifts related to sampling platform, camera parameters, illumination, and deployment environment are expected to impact model performance
- Evaluation was performed on an IID subset of available training data as well as out-of-distribution data
Training Details
- Training run:
yolo11x_megalodon_e70_p10 - Max epochs: 70, early stopping patience: 10
- Training stopped at epoch 63
- Best checkpoint: epoch 53 (highest mAP50-95)
- Best mAP50-95: 0.578
- Best mAP50: 0.799
- Full hyperparameters: see
args.yaml - Per-epoch metrics: see
results.csv
Training and Evaluation Data
- All publicly-available data on FathomNet
- Training/evaluation dataset: FathomNet/2024-megalodon-benchmark
Deployment
- Clone this repository
- In an environment with the
ultralyticsPython package installed, run:
yolo predict model=mbari-megalodon-yolo11x.pt
Sources
1 source
Sources
1 sourceVérifié 28 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- license: cc-by-4.0 tags: - ocean - object-detection - object-localization - single-class - marine datasets: - FathomNet/2024-megalodon-benchmark library_name: yolov26 --- # FathomNet Megalodon Detector ## Model Details - Trained by researchers at the [Monterey Bay Aquarium Research Institute](https://www.mbari.org/) (MBARI). - Ultralytics [YOLO11x](https://github.com/ultralytics/ultralytics) - Object detection model - Fine-tuned to detect 1 class, called 'object', using all FathomNet localizations - Updated from the `yolo11x_megalodon_e70_p10` training run ## Intended Use - Post-process video and images collected by marine researchers - Can be used to build a localized set of training images, when neither training data nor a model exists for the imagery being analyzed ## Factors - Distribution shifts related to sampling platform, camera parameters, illumination, and deployment environment are expected to impact model performance - Evaluation was performed on an IID subset of available training data as well as out-of-distribution data ## Training Details - Training run: `yolo11x_megalodon_e70_p10` - Max epochs: 70, early stopping patience: 10 - Training stopped at epoch 63 - Best checkpoint: **epoch 53** (highest mAP50-95) - Best mAP50-95: 0.578 - Best mAP50: 0.799 - Full hyperparameters: see `args.yaml` - Per-epoch metrics: see `results.csv` ## Training and Evaluation Data - All publicly-available data on [FathomNet](https://fathomnet.org/) - Training/evaluation dataset: [FathomNet/2024-megalodon-benchmark](https://huggingface.co/datasets/FathomNet/2024-megalodon-benchmark) ## Deployment 1. Clone this repository 2. In an environment with the [`ultralytics` Python package](https://github.com/ultralytics/ultralytics) installed, run: ```bash yolo predict model=mbari-megalodon-yolo11x.pt ```
Source context: 52 downloads · 0 likes · Pipeline object-detection · Library yolov26 · Repo FathomNet/megalodon-2024-yolov11
Source context: 11 downloads · 0 likes · Pipeline object-detection · Library yolov26 · Repo FathomNet/megalodon-2024-yolov11
