Efficientdet - Scalable Object Detection

# EfficientDet - Scalable Object Detection

## Introduction & Motivation

EfficientDet: scale object detection efficiently. Compound scaling rule for architecture. Applications: efficient detection across devices.

Motivation: Uniform scaling of backbone, feature network, box/class network.

Applications: Mobile detection, edge deployment.

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## Core Concepts & Theory

### Compound Scaling

Balance network depth, width, resolution.

### BiFPN

Bidirectional feature pyramid network.

### EfficientNet Backbone

Efficient base architecture.

### Multi-Level Fusion

Combine features at different scales.

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## Mathematical Formulation

Compound Scaling:
$$ ext{depth} = \phi, ext{width} = 1.1^{\phi}, ext{resolution} = 1.15^{\phi}$$

BiFPN Node:
$$ ext{output} = ext{Conv}(\sum_i w_i \cdot ext{input}_i) / (\sum_j w_j + \epsilon)$$

Detection Loss:
$$\mathcal{L} = \mathcal{L}_{ ext{cls}} + \lambda \mathcal{L}_{ ext{loc}}$$

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## Advanced Theory & Extensions

### Anchor-Free Variants

Remove anchor dependency.

### Soft Training

Gradient-friendly objective.

### Knowledge Distillation

Compress to smaller models.

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## Computational Considerations

Backbone: O(H·W·D).

BiFPN: O(H·W·C).

Detection heads: O(A·C).

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## Practical Implementation Strategies

### Architecture Search

Find optimal compound scaling.

### Feature Fusion

Weighted sum in BiFPN.

### Anchor Configuration

Scale-aware anchor selection.

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## Benchmark Datasets & Evaluation

COCO: Object detection benchmark.

Inference speed: Latency benchmarks.

Model size: Parameter count.

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## Key Challenges & Limitations

### Compound Scaling

Finding optimal balance.

### BiFPN Complexity

Weighted fusion overhead.

### Cross-Device Optimization

Hardware-specific tuning.

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## Hyperparameter Tuning

Compound phi: 0-7.

BiFPN weight: 0.1-1.0.

Learning rate: 1e-4 to 1e-3.

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## Real-World Applications & Case Studies

Mobile Detection: On-device inference.

Edge Devices: Embedded systems.

Real-time Video: Stream processing.

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## Integration with Other Methods

EfficientDet + knowledge distillation; + quantization for further compression.

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## Summary & Key Takeaways

EfficientDet scales detection via compound rules.

Principles:
1. Compound scaling: Balanced architecture.
2. BiFPN: Efficient multi-scale fusion.
3. EfficientNet: Efficient backbone.
4. Weighted fusion: Learnable feature combination.
5. Scalability: From mobile to GPU.

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## Appendix: Practical Labs

### Lab 1: Compound Scaling

import numpy as np

def compute_efficientdet_size(phi):
 """Compute EfficientDet dimensions from compound coefficient"""
 depth_base = [3, 4, 5, 6, 7, 7, 8, 8]
 width_base = [64, 88, 112, 160, 224, 288, 384, 384]
 resolution_base = [512, 640, 768, 896, 1024, 1280, 1280, 1536]
 
 depth = depth_base[min(phi, 7)]
 width = int(width_base[min(phi, 7)] * (1.1 ** (phi % 1)))
 resolution = int(resolution_base[min(phi, 7)] * (1.15 ** (phi % 1)))
 
 return depth, width, resolution

for phi in [0, 3, 6]:
 d, w, r = compute_efficientdet_size(phi)
 assert d > 0 and w > 0 and r > 0
print("✓ Compound scaling working")

### Lab 2: BiFPN Fusion

import numpy as np

def bifpn_fusion(features, weights):
 """Fuse features using weighted BiFPN node"""
 # Normalize weights
 normalized_weights = weights / (np.sum(weights) + 1e-8)
 
 # Weighted sum
 fused = np.zeros_like(features[0])
 for i, feat in enumerate(features):
 fused += normalized_weights[i] * feat
 
 return fused

np.random.seed(42)
feat1 = np.random.randn(64, 64, 256)
feat2 = np.random.randn(64, 64, 256)
feat3 = np.random.randn(64, 64, 256)
weights = np.array([1.0, 1.0, 1.0])

fused = bifpn_fusion([feat1, feat2, feat3], weights)
assert fused.shape == feat1.shape
print("✓ BiFPN fusion working")

### Lab 3: Multi-Scale Detection Head

import numpy as np

def efficientdet_detection_head(features, num_classes, num_anchors=9):
 """Detect objects at multiple scales"""
 detections = []
 
 for feat in features:
 h, w, c = feat.shape
 
 # Class predictions
 cls_pred = np.random.randn(h, w, num_anchors, num_classes) * 0.01
 
 # Box predictions
 box_pred = np.random.randn(h, w, num_anchors, 4) * 0.01
 
 detections.append((cls_pred, box_pred))
 
 return detections

np.random.seed(42)
feat_p3 = np.random.randn(64, 64, 256)
feat_p4 = np.random.randn(32, 32, 256)
feat_p5 = np.random.randn(16, 16, 256)

dets = efficientdet_detection_head([feat_p3, feat_p4, feat_p5], 80)
assert len(dets) == 3
print("✓ Multi-scale detection head working")

### Lab 4: Anchor Scaling

import numpy as np

def scale_anchors(base_anchors, scales):
 """Scale anchors for different feature levels"""
 scaled = []
 
 for scale in scales:
 scaled_anchors = base_anchors * scale
 scaled.append(scaled_anchors)
 
 return scaled

base = np.array([1.0, 1.0, 2.0, 2.0])
scales = [8, 16, 32, 64, 128]
scaled = scale_anchors(base, scales)
assert len(scaled) == len(scales)
print(f"✓ Anchor scaling: {len(scaled)} levels")

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