Yolo11m Esa Coral Icra Detector

--- language: - en library_name: ultralytics license: agpl-3.0 base_model: - Ultralytics/YOLO11 tags: - yolo - yolo11 - yolo11m - coral - icra - sea - marine-detection datasets: - NMFS-OSI/NOAA-PIFSC-ESD-ESA-CORAL-ICRA-Dataset pipeline_tag: object-detection widget: - src: https://huggingface.co/NMFS-OSI/yolo11m-esa-coral-icra-detector/resolve/main/example.jpg example_title: Example 1 --- <img align="right" src="./example.jpg"> # Yolo11m ESA Coral ICRA Detector ## Model Details This model was trained to identify and locate **Isopora crateriformis (ICRA)** using the ultralytics YOLO11 architecture in various underwater conditions. - **Model Architecture**: YOLO11m - **Task**: Object Detection (Detection) - **Classes**: 1 - ICRA (Isopora crateriformis) - **Data Type**: Underwater Optical Imagery ## Model Weights - The model's weights can be found here: **[yolo11-esa-icra-detector.pt](./yolo11-esa-icra-detector.pt)** ## Try a Live Demo here: [NMFS-OSI/ESA-Coral-ICRA-Detector-Demo](https://huggingface.co/spaces/NMFS-OSI/ESA-Coral-ICRA-Detector-Demo) ## Models Intended Use - Detection of ICRA on underwater optical data # Dataset The training data was collected, parsed, sorted, augmented, and organized from various NOAA Missions: - [Google Cloud Link: Dataset](https://console.cloud.google.com/storage/browser/nmfs_odp_pifsc/PIFSC/ESD/ARP/Photogrammetric%20Imagery/ESA_Sites) - [METADATA](https://www.fisheries.noaa.gov/inport/item/76360) ### Dataset Composition: - Multi-source Dataset: Trained on datasets that include images from various angles. - **TOAL Images**: 470 images - **Training Images**: 327 - **Validation Images**: 71 - **Test Images**: 72 - **Train/Val/Test Split Ratio**: 7:2:1 ### Data Augmentations Used To improve generalization in underwater environments and to increase dataset size, the following augmentations were applied: - Mosaic: Enabled (Closed last 40 epochs). - HSV Color Jitter: Hue (0.015), Saturation (0.4), Value (0.3). - Scaling: Random scaling (0.5). - MixUp: Disabled to prevent unrealistic object overlap. ## Model Performance - Model Architecture (YOLO11m): Lightweight and optimized for real-time detection in underwater footage. # Metrics Below are the key metrics from the model evaluation on the validation set:  ## Results (100 epochs) - Best epoch: 84 - mAP50 (B) 0.80353 - Mean average precision ca...