RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detection

Over the last few decades, many architectures have been developed that harness the power of neural networks to detect objects in near real-time. Training such systems requires substantial time across multiple GPUs and massive labeled training datasets... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
Convolution
Convolutions
1x1 Convolution
Convolutions
Softmax
Output Functions
Batch Normalization
Normalization
Max Pooling
Pooling Operations
Darknet-19
Convolutional Neural Networks
YOLOv2
Object Detection Models