Duplicate from touati-kamel/forest-fire-dataset

Co-authored-by: Touati Kamel <touati-kamel@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- image-classification
+- object-detection
+- image-segmentation
+tags:
+- forest-fire
+- fire-detection
+- smoke-detection
+- wildfire
+- computer-vision
+- disaster-management
+pretty_name: Forest Fire Dataset
+size_categories:
+- 10K<n<100K
+---
+
+# Forest Fire Dataset
+
+## Dataset Description
+
+This dataset contains frames extracted from forest fire videos for computer vision and machine learning applications. The dataset is designed for tasks such as fire detection, smoke detection, and wildfire monitoring.
+
+## Dataset Structure
+
+The dataset is organized into three splits following standard machine learning practices:
+
+```
+forest-fire-dataset/
+├── train/
+│   ├── train_00000-08999/
+│   ├── train_09000-17999/
+│   └── train_18000-26999/
+├── val/
+│   └── val_00000-08999/
+└── test/
+    └── test_00000-08999/
+```
+
+### Split Statistics
+
+| Split | Number of Frames | Percentage |
+|-------|-----------------|------------|
+| Train | 51,882 frames  | 75%        |
+| Validation | 10,363 frames   | 15%        |
+| Test  | 6,909 frames   | 10%        |
+| **Total** | **69,154 frames** | **100%** |
+
+## Data Collection
+
+- **Source**: 21 forest fire videos
+- **Format**: JPEG images
+- **Total Frames**: 69,154 frames
+- **Extraction**: All frames extracted from each video
+- **Preprocessing**: Frames were shuffled (seed=42) before splitting to ensure variety across videos in each set
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("touati-kamel/forest-fire-dataset")
+
+# Access specific splits
+train_data = dataset['train']
+val_data = dataset['val']
+test_data = dataset['test']
+```
+
+### Using with PyTorch
+
+```python
+from torch.utils.data import Dataset, DataLoader
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import os
+
+class ForestFireDataset(Dataset):
+    def __init__(self, split='train', transform=None):
+        self.split = split
+        self.transform = transform
+        # Load your frames here
+        
+    def __len__(self):
+        return len(self.frames)
+    
+    def __getitem__(self, idx):
+        image = Image.open(self.frames[idx])
+        if self.transform:
+            image = self.transform(image)
+        return image
+
+# Create dataloaders
+train_dataset = ForestFireDataset(split='train', transform=your_transform)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+```
+
+### Using with TensorFlow
+
+```python
+import tensorflow as tf
+
+def load_and_preprocess_image(image_path):
+    image = tf.io.read_file(image_path)
+    image = tf.image.decode_jpeg(image, channels=3)
+    image = tf.image.resize(image, [224, 224])
+    image = image / 255.0  # Normalize
+    return image
+
+# Create dataset
+train_ds = tf.data.Dataset.list_files('train/*/*.jpg')
+train_ds = train_ds.map(load_and_preprocess_image)
+train_ds = train_ds.batch(32).prefetch(tf.data.AUTOTUNE)
+```
+
+## Applications
+
+This dataset can be used for various computer vision tasks:
+
+- **Fire Detection**: Binary classification (fire/no-fire)
+- **Smoke Detection**: Identifying smoke patterns
+- **Severity Assessment**: Classifying fire intensity levels
+- **Temporal Analysis**: Tracking fire progression across video frames
+- **Segmentation**: Pixel-level fire and smoke segmentation
+- **Object Detection**: Detecting and localizing fire regions
+
+## Data Organization
+
+- Frames are organized in subdirectories with max 9,000 files each to comply with repository limits
+- Naming convention: `{split}_frame_{index:06d}.jpg`
+- Files are shuffled across the original 21 videos to ensure diversity in each split
+
+## Preprocessing Recommendations
+
+For best results, consider the following preprocessing steps:
+
+1. **Resize**: Standardize image dimensions (e.g., 224x224, 256x256)
+2. **Normalization**: Scale pixel values to [0, 1] or standardize using ImageNet statistics
+3. **Data Augmentation** (training only):
+   - Random horizontal flips
+   - Random rotation (±15°)
+   - Color jittering
+   - Random crops
+
+```python
+from torchvision import transforms
+
+train_transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.RandomHorizontalFlip(),
+    transforms.RandomRotation(15),
+    transforms.ColorJitter(brightness=0.2, contrast=0.2),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                       std=[0.229, 0.224, 0.225])
+])
+```
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{forest_fire_dataset_2025,
+  author = {Touati Kamel},
+  title = {Forest Fire Dataset},
+  year = {2025},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/datasets/touati-kamel/forest-fire-dataset}
+}
+```
+
+## License
+
+This dataset is licensed under the Apache License 2.0.
+
+```
+Copyright 2025 Touati Kamel
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this dataset except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+```
+
+See the [LICENSE](LICENSE) file for the full license text.
+
+## Acknowledgments
+
+- Dataset created from 21 forest fire video sources
+- Frames extracted and organized for machine learning research
+- Shuffled split ensuring diverse representation across all sets
+
+## Contact
+
+For questions, issues, or contributions, please contact the dataset maintainer or open an issue on the repository.
+
+## Version History
+
+- **v1.0** (2025): Initial release with 69,154 frames from 21 videos, organized into train/val/test splits (75/15/10)
+
+---
+
+**Note**: This dataset is intended for research and educational purposes. Please ensure compliance with relevant regulations when using for commercial applications.