Update readme.md

README.md

@@ -10,83 +10,61 @@ tags:
 - smoke
 - fire
 - yolo
-- boundybox
+- bounding-box
 - yolo26
 ---
 
-# Wildfire Smoke and Flame Detection – YOLOv26
+# Wildfire Smoke and Flame Detection – YOLOv26m (Updated)
 
 ## Overview
 
-This model is based on **YOLOv26m** and has been specifically trained to detect **wildfire smoke and flames** in outdoor environments.
+This model is based on the **YOLOv26m** architecture and has been specifically fine-tuned to detect **wildfire smoke and flames** in outdoor environments.
 
-It is designed for early wildfire detection using standard motorized RGB surveillance cameras positioned at strategic observation points such as hills, towers, or remote monitoring stations.
-
-The system operates by continuously capturing images while the camera rotates across the monitored area. Each captured frame is processed by the model to automatically identify potential fire events.
+Compared to previous versions, this update utilizes a larger dataset and optimized training hyperparameters, resulting in a significant boost in **Recall** and **mAP**. It is designed for early wildfire detection using standard RGB surveillance cameras positioned at strategic observation points such as hills, fire towers, or remote monitoring stations.
 
 ## 🎯 Purpose
 
-The primary goal of this model is **early wildfire detection**, enabling faster response times and reducing environmental and economic damage.
-
-It is optimized for:
-
-- Daytime wildfire detection  
-- Nighttime wildfire detection  
-- Smoke recognition  
-- Flame detection  
-- Standard RGB camera imagery (no thermal sensors required)
-
-## 🛰️ Deployment Scenario
+The primary goal of this model is **early wildfire detection**, enabling faster response times and reducing environmental and economic damage. 
 
-Typical workflow:
-
-1. A motorized camera rotates and captures images at regular intervals.
-2. Images are sent to the inference system.
-3. The model analyzes each frame.
-4. If smoke or flames are detected, an alert can be triggered.
+Optimized for:
+- **Daytime & Nighttime** wildfire detection.
+- **Smoke Recognition:** Critical for long-distance early detection.
+- **Flame Detection:** For immediate confirmation of fire events.
+- **Standard RGB Imagery:** Works with standard optical cameras (no thermal sensors required).
 
 ## 🧠 Model Architecture
 
-- Architecture: YOLOv26m (fused)
-- Task: Object Detection
-- Classes: `smoke`, `fire`
-- Layers: 132  
-- Parameters: 20,350,994  
-- GFLOPs: 67.9  
-- Input: RGB images  
-
-## 📊 Model Performance
+- **Architecture:** YOLOv26m (fused)
+- **Task:** Object Detection
+- **Classes:** `smoke`, `fire`
+- **Layers:** 132
+- **Parameters:** 20,350,994
+- **GFLOPs:** 67.9
+- **Input Resolution:** 640x640 (RGB)
 
-Validation set:
-- Images: 100
-- Instances: 301
+## 📊 Model Performance (Latest Validation)
 
-### Overall Metrics
+The model was trained for **80 epochs** using **Automatic Mixed Precision (AMP)** and **AdamW** optimizer. 
 
-| Metric        | Value |
-|--------------|-------|
-| Precision (P) | 0.632 |
-| Recall (R)    | 0.509 |
-| mAP@0.50      | 0.539 |
-| mAP@0.50:0.95 | 0.306 |
+### Overall Metrics (Validation Set)
+| Metric | Value | Improvement vs Previous |
+| :--- | :--- | :--- |
+| **Precision (P)** | 0.635 | +0.3% |
+| **Recall (R)** | **0.681** | **+17.2%** |
+| **mAP @0.50** | **0.675** | **+13.6%** |
+| **mAP @0.50:0.95** | **0.425** | **+11.9%** |
 
 ### Per-Class Metrics
+| Class | Images | Instances | Precision | Recall | mAP@0.50 | mAP@0.50:0.95 |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| **Smoke** | 180 | 253 | 0.633 | 0.767 | 0.752 | 0.491 |
+| **Fire** | 123 | 836 | 0.637 | 0.596 | 0.598 | 0.359 |
 
-| Class  | Images | Instances | Precision | Recall | mAP@0.50 | mAP@0.50:0.95 |
-|--------|--------|-----------|-----------|--------|----------|---------------|
-| smoke  | 97     | 125       | 0.715     | 0.722  | 0.748    | 0.475         |
-| fire   | 34     | 176       | 0.550     | 0.295  | 0.330    | 0.138         |
-
-### Inference Speed (per image)
-
-- Preprocess: 0.3 ms  
-- Inference: 12.7 ms  
-- Postprocess: 1.3 ms  
-- Total: ~14.3 ms per image  
-
-Approx. ~70 FPS theoretical throughput on Tesla T4 GPU.
-
-This model does not replace professional fire detection systems or human supervision.
+### Inference Speed (Tesla T4 GPU)
+- **Pre-process:** 0.3 ms
+- **Inference:** 12.3 ms
+- **Post-process:** 0.8 ms
+- **Total Throughput:** ~74 FPS (theoretical)
 
 ## 🚀 Inference Example (CPU)
 
@@ -95,27 +73,27 @@ Minimal example to run inference on **CPU** and print detection results.
 ```python
 from ultralytics import YOLO
 
-# Load trained model
+# Load the trained model
 model = YOLO("wildfire-smoke-fire.pt")
 
-# Run inference on CPU
-results = model("image.jpg", device="cpu")
-
-# Ultralytics returns a list (even for a single image)
-r = results[0]
-
-# Print detected boxes
-print("Detected boxes:")
-print(r.boxes)
+# Run inference on an image
+results = model("forest_view.jpg", conf=0.25)
 
-# Print class names
-print("Class names:")
-print(r.names)
+# Process results
+for r in results:
+    print(f"Detected {len(r.boxes)} objects.")
+    r.show()  # Display annotated image
 ```
 
-### Notes
 
-- Install Ultralytics:
+## 🚀 Usage Example
+
+Install the required library:
 ```bash
 pip install ultralytics
-```
+```
+
+# 🛠️ Training Configuration
+- Optimizer: AdamW (lr=0.001667, momentum=0.9)
+- Augmentations: Blur, MedianBlur, ToGray, CLAHE
+- Environment: PyTorch 2.1.0+cu128 on Tesla T4