SmokeGuardModel

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"
}

best_checkpoint.pt

pt · 40.3 MB · SHA-256 f6f8c39a73f4…84f6 · Hugging Face

--- license: mit tags: - yolov5 - smoking-detection - object-detection - onnx - pytorch --- # 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 ```python import torch model = torch.hub.load("ultralytics/yolov5", "custom", path="best_checkpoint.pt") results = model("image.jpg") results.show() ``` ### ONNX Runtime ```python 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 ```bibtex @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" } ```