Instructions to use ogaith/YOLOv8s-dog-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use ogaith/YOLOv8s-dog-detection with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("ogaith/YOLOv8s-dog-detection") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
🐶 YOLOv8 Dog Detector
This repository contains a finetuned YOLOv8 model for detecting dogs in images.
- Model:
best.pt - Dataset: Google Open Images v7 (~4,000 dog instances)
- Task: Object Detection (1 class:
dog)
📦 Installation
pip install ultralytics
🚀 CLI Usage (recommended for quick testing)
yolo detect predict \
model=best.pt \
source="assets/runs/detect/val/example1.jpg" \
imgsz=640 \
conf=0.25 \
save=True
Batch infer on folder
yolo detect predict model=best.pt source="images_folder/" imgsz=640 conf=0.25 save=True
🐍 Python Usage
from ultralytics import YOLO
# Load model
model = YOLO("best.pt")
# Run prediction
results = model.predict(
source="assets/runs/detect/val/example1.jpg",
conf=0.25,
imgsz=640
)
# Display or save result
results[0].show() # display
# results[0].save() # save to file
- Downloads last month
- 21
Model tree for ogaith/YOLOv8s-dog-detection
Base model
Ultralytics/YOLOv8