license: apache-2.0
datasets:
- Smolry/HSRP_classification_data
language:
- en
metrics:
- name: F1
type: f1
value: 0.86
- name: Precision
type: precision
value: 0.87
- name: Recall
type: recall
value: 0.85
- name: Accuracy
type: accuracy
value: 0.87
base_model:
- google/efficientnet-b0
pipeline_tag: image-classification
tags:
- computer-vision
- license-plate
- hsrp
- traffic
library_name: pytorch
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
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
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:
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
EfficientNet-B0
↓
Frozen Backbone
↓
Train Classifier Only
Optimizer:
Adam
Learning Rate:
1e-4
Epochs:
5
Scheduler:
ReduceLROnPlateau
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
Evaluation
Test Metrics
| Metric | Score |
|---|---|
| Accuracy | 87.47% |
| Precision | 87.92% |
| Recall | 85.85% |
| F1 Score | 86.87% |
Test Samples: 439
Confusion Matrix
Training History
The model was trained in two stages:
- Classifier training with the EfficientNet-B0 backbone frozen.
- Fine-tuning of the final feature blocks.
| Phase 1 Loss | Phase 2 Loss |
|---|---|
 |
 |
The epoch-level training history is available in
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
model/
└── efficientnet_b0_finetuned.pth
evaluation/
├── metrics.json
├── classification_report.txt
└── confusion_matrix.png
training/
├── training_history.json
└── phase1_loss.png
└── phase2_loss.png
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