Object detection¶
R-CNN¶
ConvNet Region ConvNet Object Detection
R-CNN (Region-based CNN) detects objects by first generating region proposals using Selective Search, then classifies each using a shared CNN. It combines region warping, feature extraction, and per-region classification with Non-Maximum Suppression.
Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition (2014): 580-587.
Name |
Model |
Input Shape |
Parameter Count |
FLOPs |
|---|---|---|---|---|
R-CNN |
\((N,C_{in},H,W)\) |
\(\mathcal{O}(P_{\text{cnn}} + F^2 + F \cdot K)\) |
\(\mathcal{O}\left(P_{\text{cnn}} + N \cdot (FHW + F^2 + FK)\right)\) |
To be implemented…🔮