Smolry/HSRP-classification

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.

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Problem Statement

Determine whether a detected vehicle license plate is an HSRP plate.

Input: License Plate Crop

Output: HSRP Probability

Decision: HSRP / Non-HSRP

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Pipeline Role

Camera Frame
      ↓
Vehicle Detection
      ↓
License Plate Detection
      ↓
Plate Crop
      ↓
┌──────────────────────┐
│ EfficientNet-B0    │
│ HSRP Classifier    │
└──────────┬───────────┘
           ↓
     HSRP Probability
           ↓
     Downstream Logic

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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

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Model Architecture

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

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Dataset

Dataset Repository:

Classes:

0 → HSRP 1 → Non-HSRP

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Training Procedure

Preprocessing

Training:

• Resize (224 × 224)
• ColorJitter
• RandomRotation (±5°)
• ImageNet Normalization

Validation / Test:

• Resize (224 × 224)
• ImageNet Normalization

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Phase 1 – Classifier Training

EfficientNet-B0
        ↓
Frozen Backbone
        ↓
Train Classifier Only

Optimizer:
    Adam

Learning Rate:
    1e-4

Epochs:
    5

Scheduler:
    ReduceLROnPlateau

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Phase 2 – Fine Tuning

Load Best Phase-1 Checkpoint
        ↓
Unfreeze Final Feature Blocks
        ↓
Fine Tune Model

Optimizer:
    Adam

Learning Rate:
    1e-5

Epochs:
    20

Scheduler:
    ReduceLROnPlateau

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Evaluation

Test Metrics

Metric	Score
Accuracy	87.47%
Precision	87.92%
Recall	85.85%
F1 Score	86.87%

Test Samples: 439

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Confusion Matrix

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Training History

The model was trained in two stages:

1. Classifier training with the EfficientNet-B0 backbone frozen.
2. Fine-tuning of the final feature blocks.

Phase 1 Loss	Phase 2 Loss

The epoch-level training history is available in

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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.

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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.

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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

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Files

model/
└── efficientnet_b0_finetuned.pth

evaluation/
├── metrics.json
├── classification_report.txt
└── confusion_matrix.png

training/
├── training_history.json
└── phase1_loss.png
└── phase2_loss.png

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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.

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Version History

v1.0

• Initial EfficientNet-B0 implementation
• Two-stage training pipeline
• Binary HSRP classification

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