Update README.md

README.md

@@ -1,4 +1,4 @@
-
+---
 pretty_name: "Deep Learning Project"
 language:
   - en
@@ -21,67 +21,75 @@ tags:
 
 # Deep Learning Project
 
-## Overview
+## Dataset Summary
+
+This repository contains the datasets, trained models, notebooks, experiments, feature-extraction outputs, and supporting resources developed for a deep learning project focused on **fire detection, fire severity classification, and related computer vision tasks**.
+
+The project covers multiple stages of a deep learning workflow, including binary fire classification, three-class fire severity classification, feature extraction, dimensionality reduction, clustering, and recommendation generation.
+
+The repository contains approximately **2.1 GB of files across 1,700+ files**.
 
-This repository contains the datasets, trained models, notebooks, feature-extraction outputs, experiments, and supporting resources developed as part of a deep learning project focused primarily on **fire detection, fire severity classification, and related computer vision tasks**.
+## Dataset Details
 
-The project brings together multiple stages of an end-to-end deep learning workflow, including binary fire classification, multi-class severity detection, feature extraction, dimensionality reduction, clustering, recommendation generation, and supporting application components.
+### Dataset Description
 
-The repository contains approximately **2.1 GB of data and project artifacts across 1,700+ files**.
+The repository is a collection of datasets and machine-learning artifacts rather than a single standardized dataset. It contains resources used across multiple deep learning experiments and application components.
 
-## Project Components
+The main components include:
 
-The repository is organized into several major components:
+- Fire vs. No-Fire binary image classification
+- Three-class fire severity classification
+- Feature extraction
+- Severity clustering
+- Dimensionality reduction
+- Recommendation generation
+- Generated severity-image samples
+- Jupyter notebooks
+- Trained models and model checkpoints
+- Supporting application resources
 
-### 1. Fire vs. No-Fire Binary Classification
+### Main Project Components
+
+#### Fire vs. No-Fire Binary Classification
 
 `Fire_vs_No_Fire_Binary_Classification/`
 
-This section contains resources related to binary image classification for distinguishing between:
+Contains resources for binary image classification between:
 
 - Fire
 - No Fire
 
-Multiple deep learning architectures and experiments are included, such as:
+The project includes experiments using:
 
 - ResNet50
 - Custom CNN
 - VGG16
 - EfficientNetB0
 
-The directory also contains model checkpoints and the associated dataset used for binary classification experiments.
+The directory also contains a dataset, trained model resources, and VGG16 checkpoints.
 
-### 2. Fire Severity Detection
+#### Fire Severity Detection
 
 `Severity_Detection_Tri_Classification/`
 
-This section focuses on **three-class fire severity classification**.
-
-The project includes severity categories such as:
+Contains resources for three-class fire severity classification:
 
 - Mild
 - Moderate
 - Severe
 
-The directory contains experiments involving architectures including:
+The project includes experiments using:
 
 - Xception
 - EfficientNetB0
 
-It also contains supporting work for:
+Additional components include feature extraction, clustering, dimensionality reduction, and dataset preparation.
 
-- Feature extraction
-- Severity clustering
-- Dimensionality reduction
-- Dataset preparation and modification
-
-### 3. Severity Dataset
+#### Severity Dataset
 
 `Severity_Detection_Tri_Classification/Severity_Altered_Dataset/`
 
-The project contains an image dataset organized into training, validation, and testing subsets.
-
-The dataset is structured by severity class:
+Contains an image dataset organized into training, validation, and testing splits.
 
 ```text
 severity_dataset/
@@ -96,185 +104,4 @@ severity_dataset/
 └── test/
     ├── mild/
     ├── moderate/
-    └── severe/
-````
-
-This structure is suitable for supervised image-classification workflows.
-
-### 4. Feature Extraction
-
-`Severity_Detection_Tri_Classification/Feature_Extraction/`
-
-Contains intermediate feature representations generated during the severity-classification workflow.
-
-These extracted features can be used for downstream analysis and machine-learning experiments without necessarily repeating the original image feature-extraction process.
-
-### 5. Severity Clustering
-
-`Severity_Detection_Tri_Classification/Severity_Clustering/`
-
-Contains resources associated with clustering and exploratory analysis of extracted fire-related features.
-
-### 6. Dimensionality Reduction
-
-`Severity_Detection_Tri_Classification/Dimentionality_Reduction/`
-
-Contains experiments and outputs related to dimensionality-reduction techniques applied to extracted feature representations.
-
-### 7. Recommendation Generation
-
-`Recommendation_Generation/`
-
-Contains resources related to generating recommendations based on the outputs of the fire detection and/or severity analysis pipeline.
-
-### 8. Generated Severity Images
-
-`Generated_Severity_Images_Sample/`
-
-Contains sample generated or processed images associated with the severity-detection component of the project.
-
-### 9. Notebooks
-
-`Notebooks/`
-
-Contains Jupyter notebooks used during experimentation, analysis, preprocessing, model development, and evaluation.
-
-### 10. EmberDeepAI
-
-`EmberDeepAI/`
-
-Contains the associated project/application component developed around the deep learning work.
-
-## Intended Use
-
-This repository can be used for:
-
-* Fire vs. no-fire image classification
-* Fire severity classification
-* Transfer-learning experiments
-* Deep-learning model experimentation
-* Computer vision research
-* Feature extraction and representation analysis
-* Clustering and dimensionality-reduction experiments
-* Development of fire-analysis pipelines
-* Educational and research purposes
-
-## Models and Architectures
-
-The project includes experiments involving several convolutional neural network architectures, including:
-
-* **ResNet50**
-* **VGG16**
-* **EfficientNetB0**
-* **Xception**
-* **Custom CNN architectures**
-
-The repository may contain trained model files, checkpoints, intermediate outputs, and experiment-specific artifacts.
-
-## Dataset Structure
-
-The repository is not a single flat dataset. Instead, it is a collection of datasets and machine-learning artifacts organized according to their respective experiments and project components.
-
-A simplified representation is:
-
-```text
-DeepLearningProject/
-│
-├── EmberDeepAI/
-│
-├── Fire_vs_No_Fire_Binary_Classification/
-│   ├── ResNet50/
-│   ├── Custom_CNN/
-│   ├── VGG16_finalized/
-│   │   └── checkpoints/
-│   ├── EfficientNetB0/
-│   └── Dataset/
-│
-├── Generated_Severity_Images_Sample/
-│
-├── Notebooks/
-│
-├── Recommendation_Generation/
-│
-├── Severity_Detection_Tri_Classification/
-│   ├── Xception_finalized/
-│   ├── Feature_Extraction/
-│   ├── Severity_Clustering/
-│   ├── EfficientNetB0/
-│   ├── Dimentionality_Reduction/
-│   └── Severity_Altered_Dataset/
-│       └── severity_dataset/
-│           ├── train/
-│           ├── val/
-│           └── test/
-│
-└── README.md
-```
-
-## Data Organization
-
-The image-classification datasets are organized using directory-based class labels. This structure is compatible with common TensorFlow/Keras image-data loading workflows.
-
-For example:
-
-```text
-train/
-├── mild/
-├── moderate/
-└── severe/
-```
-
-Each directory represents a target classification label.
-
-## Preprocessing
-
-Preprocessing procedures vary between experiments and may include image preparation, dataset modification, feature extraction, dimensionality reduction, and other experiment-specific transformations.
-
-The accompanying notebooks and project directories provide additional context regarding individual preprocessing and experimentation workflows.
-
-## Limitations
-
-This repository represents a collection of project datasets and experimental artifacts rather than a standardized benchmark dataset.
-
-Important considerations include:
-
-* Dataset composition may differ between individual experiments.
-* Different models may have been trained using different preprocessing pipelines.
-* Some directories contain intermediate or generated artifacts rather than raw data.
-* Model performance may depend heavily on preprocessing, class distribution, and training configuration.
-* The datasets should not be assumed to represent all real-world fire conditions.
-* Performance in controlled datasets may not directly translate to real-world deployment environments.
-
-## Responsible Use
-
-Fire detection and severity classification systems can be used in safety-critical contexts. Models trained using this repository should therefore be evaluated carefully before being used for real-world emergency response, public safety, or automated decision-making.
-
-Predictions should not be treated as a substitute for professional assessment or established emergency-response procedures.
-
-## Repository Contents
-
-At the time of publication, the project directory contains approximately:
-
-* **2.1 GB** of files
-* **1,700+ files**
-* Multiple image datasets
-* Trained deep-learning models and checkpoints
-* Feature-extraction outputs
-* Clustering and dimensionality-reduction experiments
-* Jupyter notebooks
-* Recommendation-generation resources
-* Supporting application/project files
-
-## Citation
-
-If you use this repository in academic work, research, or derivative projects, please cite the original project according to the associated publication or project documentation.
-
-## License
-
-No standardized open-source dataset license has been specified for the complete project collection.
-
-Please review the licensing and provenance of individual datasets, models, and third-party resources before redistributing or using them commercially.
-
-## Acknowledgements
-
-This repository consolidates the datasets, experiments, models, and supporting resources developed throughout the deep-learning project.
+    └── severe/