SmokeGuard Models
YOLOv5 models untuk deteksi aktivitas merokok secara real-time.
Models
| Model | Format | Size | Description |
|---|---|---|---|
best_checkpoint.pt |
PyTorch | ~40 MB | Model training terbaik |
last_checkpoint.pt |
PyTorch | ~40 MB | Checkpoint terakhir |
best_checkpoint.onnx |
ONNX (FP32) | ~80 MB | Export ONNX untuk inference |
best_checkpoint_int8.onnx |
ONNX (INT8) | ~20 MB | Quantized untuk CPU inference |
Usage
PyTorch
import torch
model = torch.hub.load("ultralytics/yolov5", "custom", path="best_checkpoint.pt")
results = model("image.jpg")
results.show()
ONNX Runtime
import onnxruntime as ort
import numpy as np
session = ort.InferenceSession("best_checkpoint_int8.onnx")
input_name = session.get_inputs()[0].name
input_data = np.random.randn(1, 3, 640, 640).astype(np.float32)
output = session.run(None, {input_name: input_data})
Quantization
Untuk quantization ONNX model, lihat notebook quantisize.ipynb.
Citation
@article{IPI4527801,
title = "IMPLEMENTASI METODE YOLOv5 PADA SISTEM PENDETEKSI ROKOK DI AREA BEBAS ASAP ROKOK",
journal = "Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)",
year = "2024",
author = "Fathoni, Aliffatul Majid; Zuliarso, Eri"
}