Instructions to use Vaibhavsh0120/ATM-Theft-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Vaibhavsh0120/ATM-Theft-Detection with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("Vaibhavsh0120/ATM-Theft-Detection") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
ATM Theft Detection YOLOv8 Model
This folder is intended to be pushed as a standalone Hugging Face model repository.
Model Summary
The model detects two classes for ATM monitoring:
Face_CoveredFace_Uncovered
Files
weights/best.pt: published YOLOv8 checkpointexports/best_full_integer_quant.tflite: optional quantized export copied from local training
Local Usage
from ultralytics import YOLO
model = YOLO("weights/best.pt")
results = model("example.jpg")
Source Workflow
The canonical local training flow lives in the root repository under training/. After training, push the latest model and Space snapshots with:
python scripts/push_hf.py
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