Object Detection
ultralytics
English
biology
CV
images
animals
YOLO
fine-tuned
zebra
giraffe
onager
dog
Instructions to use imageomics/mmla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use imageomics/mmla with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("imageomics/mmla", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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- object-detection
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- YOLO
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- fine-tuned
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datasets:
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- custom_animal_dataset
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metrics:
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# Model Card for Fine-Tuned YOLOv11m Animal Detection Model
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This model is a fine-tuned version of
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## Model Details
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#### Testing Data
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The model was evaluated on a held-out test set located at `
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- 7658 test images with instances of Zebra, Giraffe, Onager, and Dog
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- object-detection
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- YOLO
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- fine-tuned
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- zebra
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- giraffe
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- onager
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- dog
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datasets:
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- custom_animal_dataset
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metrics:
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# Model Card for Fine-Tuned YOLOv11m Animal Detection Model
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This model is a fine-tuned version of [YOLO11m](https://docs.ultralytics.com/models/yolo11/) optimized for detection and classification of wildlife from low-altitude drone imagery.
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It has been trained to identify zebras (Plains and Grevy's), giraffes (reticulated and Masai), Persian onagers, and African Painted dogs with high accuracy across diverse environmental conditions.
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## Model Details
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#### Testing Data
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The model was evaluated on a held-out test set located at `images/test` containing:
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- 7658 test images with instances of Zebra, Giraffe, Onager, and Dog
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