README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: detections
      list:
        - name: class
          dtype: string
        - name: bbox_xyxy
          list: float64
        - name: confidence
          dtype: float64
    - name: yolo_labels
      dtype: string
    - name: image_annotated
      dtype: image
  splits:
    - name: train
      num_bytes: 11360605942.32
      num_examples: 69018
  download_size: 11350785810
  dataset_size: 11360605942.32
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Forest Fire Detection Dataset — Auto-Annotated

Bounding-box annotated version of touati-kamel/forest-fire-dataset, built for training forest-fire / smoke / fog object detection models.

Overview

This dataset contains video frames auto-labeled with bounding boxes for fire and smoke-related visual phenomena, using a zero-shot open-vocabulary object detector (Grounding DINO). It is derived from the original touati-kamel/forest-fire-dataset image classification dataset, which did not include bounding box annotations.

Classes

Class	Description
Fire	Visible flame
Fire-smoke	Smoke originating from fire
Fog	Fog / mist in the scene
Factory-smoke	Industrial/factory smoke (non-fire smoke source)

Why a single train split?

The source frames were extracted from videos and then shuffled randomly before being split into train/validation/test. Because consecutive video frames are often 99%+ visually similar, this shuffling caused near-duplicate frames from the same video clip to end up scattered across different splits -- a data leakage problem that would make validation/test metrics unreliable (a model could "memorize" a near-identical frame seen during training).

To fix this, all annotated frames from the original train/validation/test splits have been merged into a single train split here. Validation and test splits will be added later, sourced from separate, distinct videos not present in train, to ensure clean evaluation without leakage.

Annotation methodology

• Model: IDEA-Research/grounding-dino-tiny (zero-shot, open-vocabulary object detection), run via Hugging Face transformers.
• Prompts used (mapped to class names):
  • "flame" → Fire
  • "smoke from fire" → Fire-smoke
  • "fog" → Fog
  • "industrial smoke" → Factory-smoke
• Thresholds: box confidence >= 0.30, text matching threshold >= 0.25.
• Important: these are automatically generated (teacher-model) annotations, not human-verified. Expect some false positives/negatives, especially on visually ambiguous frames (heavy haze, distant smoke, low light). Manual review or a secondary verification pass is recommended before using this data for anything beyond bootstrapping a first model.

Schema

Column	Type	Description
image	Image	Original, unannotated frame
detections	list[dict]	One entry per detected box: {"class": str, "bbox_xyxy": [x1,y1,x2,y2], "confidence": float}
yolo_labels	string	Same boxes pre-converted to YOLO format (class_id x_center y_center width height, normalized 0-1), one line per box, ready to write directly to .txt label files
image_annotated	Image (optional, some chunks)	Visual copy of image with boxes/labels drawn, for quick QA

Class-to-ID mapping for yolo_labels is stored in classes.json at the repo root: {"0": "Fire", "1": "Fire-smoke", "2": "Fog", "3": "Factory-smoke"} (order-dependent list).

Usage

from datasets import load_dataset

ds = load_dataset("touati-kamel/forest-fire-annotations")
example = ds["train"][0]
print(example["detections"])
print(example["yolo_labels"])

Converting to a YOLO training folder

import os

os.makedirs("yolo_dataset/images/train", exist_ok=True)
os.makedirs("yolo_dataset/labels/train", exist_ok=True)

for i, example in enumerate(ds["train"]):
    example["image"].save(f"yolo_dataset/images/train/{i:07d}.jpg")
    with open(f"yolo_dataset/labels/train/{i:07d}.txt", "w") as f:
        f.write(example["yolo_labels"])

Roadmap

• Add genuinely separate validation and test splits from new, distinct video sources (not derived from frames already in train).
• Optional human-in-the-loop verification pass on a sample of auto-labeled boxes to estimate label quality/precision.

Source data

Original unannotated frames: touati-kamel/forest-fire-dataset

Maintainer

Kamel Touati (HuggingFace: touati-kamel, GitHub: KamelTouati)