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Check out the documentation for more information.
Hybrid Faster R-CNN — Learnable Weighted Fusion
Model
Hybrid Faster R-CNN object detector combining:
- ResNet-101
- Inception-V3
The two backbone branches are combined using learnable weighted feature fusion.
The fusion weights are initialized at 0.5 / 0.5 and learned during training.
Architecture
Input Image → ResNet-101 → Inception-V3 → Learnable Weighted Fusion → 256-channel feature map → Faster R-CNN → Object Detection
Detector
- Framework: Detectron2
- Detector: Faster R-CNN
- Classes: 11
- Feature map: fused
- Channels: 256
- Stride: 32
- Training iterations: 30,000
Validation Results
AP: 51.3646 AP50: 84.2608 AP75: 57.2804 APm: 28.2779 APl: 52.4605 AR@100: 58.9
Baseline
The baseline model uses feature concatenation.
Baseline:
AP: 52.6155 AP50: 87.8719 AP75: 58.2183
The learnable weighted fusion therefore provides a closely comparable alternative to simple feature concatenation.
Purpose
This model represents the learnable weighted-fusion ablation used to investigate the effect of feature-fusion mechanisms in the hybrid ResNet-101 + Inception-V3 detector.
The original concatenation baseline is preserved separately.