YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

Object detection remains an active area of research in the field of computer vision, and considerable advances and successes has been achieved in this area through the design of deep convolutional neural networks for tackling object detection. Despite these successes, one of the biggest challenges to widespread deployment of such object detection networks on edge and mobile scenarios is the high computational and memory requirements... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Logistic Regression
Generalized Linear Models
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
k-Means Clustering
Clustering
Max Pooling
Pooling Operations
Softmax
Output Functions
Residual Connection
Skip Connections
Convolution
Convolutions
Darknet-19
Convolutional Neural Networks
Darknet-53
Convolutional Neural Networks
YOLOv3
Object Detection Models
YOLOv2
Object Detection Models