ModelListedExternal

Driver Monitoring

운전자 이상행동 감지를 위한 Video Swin Transformer 기반 모델입니다.

Sourcehuggingface.co/koreashin/Driver_monitoring/tree/f5247c7c2564a11abb717c2af4ea231a4a5723db

KindOtherVersionRevision f5247c7Cgrade

Model source

Kind: Other
File size: 120–163 MB
Version: vRevision f5247c7
Updated: Oct 24, 2026
Downloads: 9
Source: Hugging Face

Source description

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}
}

Sources

1 source

Hugging FacePrimaryVersion f5247c7c2564a11abb717c2af4ea231a4a5723db

huggingface.co/koreashin/Driver_monitoring/tree/f5247c7c2564a11abb717c2af4ea231a4a5723db

Verified Oct 24

Download files

Model artifacts

2 artifacts

model.onnx

onnx · 163 MB · SHA-256 5b16e8969d74…9759 · Hugging Face

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pytorch_model.bin

bin · 120 MB · SHA-256 db2e18ab37ce…ce9c · Hugging Face

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Source excerpts

3 excerpts

ReadmeAug 6huggingface.co/koreashin/Driver_monitoring

--- 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) ```python 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) 1. Copy `model.onnx` to mac...

NoteOct 24huggingface.co/koreashin/Driver_monitoring

Source context: 9 downloads · 1 likes · Pipeline video-classification · Repo koreashin/Driver_monitoring

NoteAug 6huggingface.co/koreashin/Driver_monitoring

Source context: 5 downloads · 0 likes · Pipeline video-classification · Repo koreashin/Driver_monitoring