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README.md

@@ -27,3 +27,107 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# Forest Fire Detection Dataset — Auto-Annotated
+
+Bounding-box annotated version of [touati-kamel/forest-fire-dataset](https://huggingface.co/datasets/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
+
+```python
+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
+
+```python
+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](https://huggingface.co/datasets/touati-kamel/forest-fire-dataset)
+
+## Maintainer
+
+Kamel Touati ([HuggingFace: touati-kamel](https://huggingface.co/touati-kamel),
+[GitHub: KamelTouati](https://github.com/KamelTouati))