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Datasets:
LK-ROBOTICS
/
nxp-sf-train-balanced

Tasks:
Object Detection
Modalities:
Image
Languages:
Korean
Tags:
image
yolo
patrol
safety
fire-detection
smoke-detection
License:
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nxp-sf-train-balanced / revisions /v2 /README.md
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metadata 
task_categories:
  - object-detection
license: other
tags:
  - image
  - yolo
  - patrol
  - safety
  - fire-detection
  - smoke-detection
  - sf
pretty_name: NXP SF Balanced Fire/Smoke Dataset v2
language:
  - ko
size_categories:
  - 10K<n<100K

NXP SF Balanced Fire/Smoke Dataset v2

개요

nxp_sf_train_balanced_v2는 NXP lk_vision_patroller 전면 화재/연기 탐지 후보 모델을 학습하기 위한 YOLO 형식 2-class SF(smoke/fire) 데이터셋 revision v2입니다.

이 revision은 balanced-v1 backbone을 유지하면서, 최신 버전 우선 규칙에 따라 D-family는 roboflow_psf_train_d_v3만 반영하고, e_lighter fire bias를 줄인 구성입니다.

클래스 스키마는 다음으로 고정합니다.

0: fire
1: smoke

구성

  • 포맷: YOLO detection (images/ + labels/*.txt)
  • split:
    • train/images, train/labels
    • val/images, val/labels
    • test/images, test/labels
  • 총 이미지 수: 11,936
  • 총 box 수:
    • fire: 11,800
    • smoke: 8,785
  • empty-label 이미지 수: 2,511
split images fire boxes smoke boxes empty-label images
train 9,277 9,156 6,750 2,003
val 1,493 1,541 1,175 260
test 1,166 1,103 860 248

주요 변화 (v1 -> v2)

  • 유지: kaggle_sf_train_dfire_sampled5k_v1, roboflow_sf_train_a_v1, roboflow_sf_train_b_v1
  • 유지: roboflow_sf_train_anguk_v1, roboflow_sf_train_daegu_v1
  • 조정: roboflow_sf_train_gongduk_v1 비중 소폭 증가
  • 추가: roboflow_psf_train_d_v3
  • 추가: roboflow_psf_train_gongduk_b_v2
  • 추가: roboflow_psf_train_e_lighter_v1 (축소 반영)
  • 최신 버전 우선: D-family는 v1, v2를 제외하고 v3만 사용

Hugging Face revision policy

This dataset family uses a stable repo id plus revision paths.

  • stable repo: LK-ROBOTICS/nxp-sf-train-balanced
  • this revision path: revisions/v2

사용법

Ultralytics YOLO 학습 예시:

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
model.train(
    data="/home/jinhyuk2me/lk_ws/ai/vision/perception/projects/patroller-sf-yolo11n-nxp/data-nxp-sf-train-balanced-v2.yaml",
    imgsz=320,
    epochs=200,
    patience=40,
    batch=192,
    workers=8,
)

Provenance

  • dataset id: nxp_sf_train_balanced_v2
  • build manifest: BUILD_MANIFEST.yaml
  • MMS recipe metadata: /home/jinhyuk2me/lk_ws/ops/ml-management/lk_mms/datasets/recipes/nxp-sf-train-balanced-v2.yaml
  • project YAML: /home/jinhyuk2me/lk_ws/ai/vision/perception/projects/patroller-sf-yolo11n-nxp/data-nxp-sf-train-balanced-v2.yaml