Update README.md

README.md

@@ -12,15 +12,320 @@ metrics:
 base_model:
 - google/efficientnet-b0
 
-# HSRP Classification Model
-
-## Model Description
-## This model is designed to classify High-Security Registration Plates (HSRP) from vehicle crop images.
-
 pipeline_tag: image-classification
 tags:
 - computer-vision
 - license-plate
 - hsrp
 - traffic
----
+---
+# HSRP Classification using EfficientNet-B0
+
+## Model Overview
+
+This model performs binary classification of vehicle license plate images.
+
+Given a cropped license plate image, the model predicts whether the plate belongs to:
+
+- HSRP
+- Non-HSRP
+
+The model forms the classification component of the Smart HSRP Detection and Violation Monitoring System.
+
+---
+
+## Problem Statement
+
+Determine whether a detected vehicle license plate is an HSRP plate.
+
+Input:
+    License Plate Crop
+
+Output:
+    HSRP Probability
+
+Decision:
+    HSRP / Non-HSRP
+
+---
+
+## Pipeline Role
+
+```text
+Camera Frame
+      ↓
+Vehicle Detection
+      ↓
+License Plate Detection
+      ↓
+Plate Crop
+      ↓
+┌──────────────────────┐
+│ EfficientNet-B0    │
+│ HSRP Classifier    │
+└──────────┬───────────┘
+           ↓
+     HSRP Probability
+           ↓
+     Downstream Logic
+
+```
+---
+
+## Scope
+
+This model performs:
+
+✓ HSRP Classification
+
+This model does NOT perform:
+
+✗ Vehicle Detection
+✗ License Plate Detection
+✗ OCR
+✗ License Plate Recognition
+✗ Vehicle Tracking
+✗ Violation Decision Making
+
+---
+
+## Model Architecture
+
+```text
+Backbone:
+    EfficientNet-B0
+
+Initialization:
+    ImageNet Pretrained Weights
+
+Classifier Head:
+
+Dropout(0.2)
+        ↓
+Linear(1280 → 1)
+
+Output:
+    Binary Classification Logit
+
+Activation:
+    Sigmoid
+
+Input Size:
+    224 × 224 RGB
+
+```
+---
+
+## Dataset
+
+Dataset Repository:
+
+<HF_DATASET_LINK>
+
+Classes:
+
+0 → HSRP
+1 → Non-HSRP
+
+---
+
+## Training Procedure
+
+### Preprocessing
+
+Training:
+
+- Resize (224 × 224)
+- ColorJitter
+- RandomRotation (±5°)
+- ImageNet Normalization
+
+Validation / Test:
+
+- Resize (224 × 224)
+- ImageNet Normalization
+
+---
+
+### Phase 1 – Classifier Training
+
+```text
+EfficientNet-B0
+        ↓
+Frozen Backbone
+        ↓
+Train Classifier Only
+
+Optimizer:
+    Adam
+
+Learning Rate:
+    1e-4
+
+Epochs:
+    5
+
+Scheduler:
+    ReduceLROnPlateau
+
+```
+---
+
+### Phase 2 – Fine Tuning
+
+```text
+Load Best Phase-1 Checkpoint
+        ↓
+Unfreeze Final Feature Blocks
+        ↓
+Fine Tune Model
+
+Optimizer:
+    Adam
+
+Learning Rate:
+    1e-5
+
+Epochs:
+    20
+
+Scheduler:
+    ReduceLROnPlateau
+
+```
+---
+
+## Evaluation
+
+### Test Metrics
+
+| Metric | Score |
+|----------|----------|
+| Accuracy | 87.47% |
+| Precision | 87.92% |
+| Recall | 85.85% |
+| F1 Score | 86.87% |
+
+Test Samples:
+    439
+
+---
+
+## Confusion Matrix
+
+![Confusion Matrix](evaluation/confusion_matrix.png)
+
+---
+
+## Training History
+
+Training curves are available in:
+
+training/
+
+Contents:
+
+- train_loss.png
+- validation_loss.png
+- learning_curve.png
+
+---
+
+## Explainability
+
+Grad-CAM visualizations are provided to understand which image regions contribute most strongly to predictions.
+
+Examples include:
+
+- Correct HSRP predictions
+- Correct Non-HSRP predictions
+- False Positives
+- False Negatives
+
+Location:
+
+explainability/
+
+---
+
+## Observations
+
+Key observations discovered during evaluation:
+
+- Model performs strong binary separation between classes.
+- Errors primarily occur in visually ambiguous samples.
+- Performance depends heavily on crop quality.
+- Explainability analysis should be used to validate focus on plate regions.
+
+---
+
+## Limitations
+
+- Binary classification only.
+- Not evaluated on every Indian state.
+- Performance may degrade on heavily blurred images.
+- Sensitive to plate crop quality.
+- Domain shift may affect performance.
+
+---
+
+## Future Improvements
+
+Potential future work:
+
+- Larger dataset
+- Additional state coverage
+- CCTV-specific training data
+- Hard-negative mining
+- Better augmentation
+- Threshold optimization
+- Calibration analysis
+- Lightweight deployment models
+
+---
+
+## Files
+
+```text
+model/
+└── efficientnet_b0_finetuned.pth
+
+evaluation/
+├── metrics.json
+├── classification_report.txt
+└── confusion_matrix.png
+
+training/
+├── training_history.json
+└── training_config.yaml
+
+explainability/
+└── gradcam_examples/
+
+```
+---
+
+## Project Context
+
+This model is one component of the larger Smart HSRP Detection and Violation Monitoring System.
+
+Its sole responsibility is determining whether a cropped license plate image belongs to an HSRP or Non-HSRP category.
+
+---
+
+## Version History
+
+### v1.0
+
+- Initial EfficientNet-B0 implementation
+- Two-stage training pipeline
+- Binary HSRP classification
+
+---
+
+## Author
+
+Aniket Behera
+
+Computer Engineering
+Smart HSRP Detection & Violation Monitoring System