Object Detection
ultralytics
yolo
yolov11
poultry
chicken
egg
broiler
agriculture
smart-farming
animal-welfare
precision-livestock-farming
Eval Results (legacy)
Instructions to use Williamsanderson/PoultryVision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Williamsanderson/PoultryVision with ultralytics:
from ultralytics import YOLOvv11 model = YOLOvv11.from_pretrained("Williamsanderson/PoultryVision") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Initial release: YOLOv11m fine-tuned on PoultryVision dataset (79.3% mAP50-95, beats paper YOLOv11x by +8.5pts)
eacfff4 verified Download confusion_matrix_normalized.png from Williamsanderson/PoultryVision: direct link, hf CLI and curl.
- Browser
- Download file 107 kB
-
https://huggingface.co/Williamsanderson/PoultryVision/resolve/main/confusion_matrix_normalized.png
- Command line
-
hf download hf://Williamsanderson/PoultryVision/confusion_matrix_normalized.png
-
curl -L -o confusion_matrix_normalized.png https://huggingface.co/Williamsanderson/PoultryVision/resolve/main/confusion_matrix_normalized.png
107 kB

- Xet hash:
- 392250db1abc2554ecd0bc8a546d5946f16e3300c173cdfa75677268c666b4b8
- Size of remote file:
- 107 kB
- SHA256:
- f97ad94aeb48693be8426b1d46f7ae246b905549288844947e4f49c942e814a1
·
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