TurtleDetector

--- library_name: ultralytics license: cc-by-nc-4.0 language: - en base_model: - Ultralytics/YOLO11 pipeline_tag: image-segmentation tags: - object-detection - yolo - ultralytics - turtles - head - flippers - carapace --- TurtleDetector detects sea turtles, their heads, and their flippers. For flippers, it distinguishes front/hind and left/right flippers, enabling precise matching for re-identification of individual turtles. It is able to detect sea turtles both under and above water. <img src="images/321595639_581630651.jpg" width="500"> <img src="images/321595639_581630651_seg.jpg" width="500"> ## Training Two sources were used for training: - [SeaTurtleID2022](https://www.kaggle.com/datasets/wildlifedatasets/seaturtleid2022): A large database of 438 individual loggerhead turtles spanning 13 years. All photos are underwater. - [TurtlesOfSMSRC](https://www.inaturalist.org/projects/turtles-of-smsrc): Emerging database of mostly juvenile green turtles of both underwater photos and photos from rescue centres. Only the latter were chosen to complement the SeaTurtleID2022 database. The training procedure is (very briefly) decribed in [this notebook](https://huggingface.co/BVRA/TurtleDetector/blob/main/training/segmentation_prepare.ipynb). ## Usage The model can be used as any `ultralytics` model. First, download and load the model. ``` from huggingface_hub import hf_hub_download from ultralytics import YOLO path_model = hf_hub_download( repo_id="BVRA/TurtleDetector", filename="turtle_detector.pt", ) model = YOLO(path_model) ``` Then download an image (or use yours) and run the prediction. ``` import requests from io import BytesIO from PIL import Image def load_image(url): r = requests.get(url, timeout=30) r.raise_for_status() return Image.open(BytesIO(r.content)).convert("RGB") img_url = "https://huggingface.co/BVRA/TurtleDetector/resolve/main/images/321595639_581630651.jpg" img = load_image(img_url) result = model.predict(img, verbose=False, save=False, show=False)[0] img_annotated = result.plot()[:, :, ::-1] Image.fromarray(img_annotated) ```

Source context: 236 downloads · 3 likes · Pipeline image-segmentation · Library ultralytics · Repo BVRA/TurtleDetector