koreashin/Driver_monitoring

By koreashin · ↓ 1 · ♥ 0 · Updated 2026-01-19

What's in this repo

This page lists the files in koreashin/Driver_monitoring, including model.onnx.

1 ONNX models (.onnx files)
8 files, including 2 .py, 1 .md, 1 .json, 1 .onnx, 1 .bin
Last updated 2026-01-19

ONNX files

File	Size	Download
model.onnx	—	Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

language:

ko license: apache-2.0 tags:
video-classification
driver-behavior-detection
swin-transformer
video-swin
pytorch datasets:
custom metrics:
accuracy
f1 pipeline_tag: video-classification model-index:
name: driver-behavior-swin3d-t results:
- task: type: video-classification name: Video Classification metrics:
  - type: accuracy value: 0.9805 name: Accuracy
  - type: f1 value: 0.9757 name: Macro F1

Driver Behavior Detection Model (Epoch 7)

운전자 이상행동 감지를 위한 Video Swin Transformer 기반 모델입니다.

Model Description

Architecture: Video Swin Transformer Tiny (swin3d_t)
Backbone Pretrained: Kinetics-400
Parameters: 27.85M
Input: [B, 3, 30, 224, 224] (batch, channels, frames, height, width)

Classes (5)

Label	Class	F1-Score
0	정상 (Normal)	0.97
1	졸음운전 (Drowsy Driving)	0.99
2	물건찾기 (Reaching/Searching)	0.96
3	휴대폰 사용 (Phone Usage)	0.96
4	운전자 폭행 (Driver Assault)	1.00

Performance (Epoch 7)

Metric	Value
Accuracy	98.05%
Macro F1	0.9757
Validation Samples	1,371,062

Training Configuration

Parameter	Value
Hardware	2x NVIDIA RTX A6000 (48GB)
Distributed	DDP (DistributedDataParallel)
Batch Size	32 (16 x 2 GPU)
Gradient Accumulation	4
Effective Batch	128
Optimizer	AdamW (lr=1e-3, wd=0.05)
Scheduler	OneCycleLR
Mixed Precision	FP16
Loss	CrossEntropy + Label Smoothing (0.1)
Regularization	Mixup (a=0.4), Dropout (0.3)

Files

File	Size	Description
`pytorch_model.bin`	121 MB	PyTorch weights (FP32)
`model.onnx`	164 MB	ONNX model for mobile deployment
`config.json`	1.2 KB	Model configuration
`model.py`	6.9 KB	Model architecture code
`convert_coreml_macos.py`	2.2 KB	CoreML conversion script (macOS)

Platform-specific Usage

PyTorch (Server/Desktop)

import torch
from model import DriverBehaviorModel

model = DriverBehaviorModel(num_classes=5, pretrained=False)
checkpoint = torch.load("pytorch_model.bin", map_location="cpu")
model.load_state_dict(checkpoint["model"])
model.eval()

iOS (CoreML)

Copy model.onnx to macOS
Run conversion script:

python convert_coreml_macos.py

Add generated DriverBehavior.mlpackage to Xcode project

Android (ONNX Runtime)

// build.gradle
implementation 'com.microsoft.onnxruntime:onnxruntime-android:1.16.0'

// Kotlin
val session = OrtEnvironment.getEnvironment()
    .createSession(assetManager.open("model.onnx").readBytes())

val output = session.run(mapOf("video_input" to inputTensor))

Preprocessing (All Platforms)

Input Shape: [1, 3, 30, 224, 224]  (batch, channels, frames, height, width)
Channel Order: RGB
Normalization: (pixel / 255.0 - mean) / std
  - mean = [0.485, 0.456, 0.406]
  - std = [0.229, 0.224, 0.225]
Resize: 224x224 (BILINEAR)
Frames: 30 frames uniformly sampled

Dataset

Total Videos: 243,979
Total Samples (windows): 1,371,062
Window Size: 30 frames
Stride: 15 frames
Resolution: 224x224

Training Progress

Epoch	Accuracy	Macro F1
5	97.35%	0.9666
6	97.74%	0.9720
7	98.05%	0.9757

License

This model is for research purposes only.

Citation

@misc{driver-behavior-detection-2026,
  title={Driver Behavior Detection using Video Swin Transformer},
  author={C-Team},
  year={2026}
}

Model info

video-classification

pytorch onnx video-swin-transformer video-classification driver-behavior-detection swin-transformer video-swin ko dataset:custom license:apache-2.0 model-index region:us

↓ 1 ♥ 0

Created: 2026-01-15

Updated: 2026-01-19

View on Hugging Face

Files (8)

.gitattributes

FP32 ONNX

README.md

config.json

convert_coreml_macos.py

model.onnx ONNX

model.py

pytorch_model.bin

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