By Or Tslil, Tal Feiner

This work uses an implementation of [1], forked from the repository (https://github.com/balancap/SSD-Tensorflow). SSD is an unified framework for object detection with a single network. The intuition behaynd SSD is a multiple convolution operation with different shapes and sizes, each for different abstract (depth) of the network.

SSD architecture [1].

The output of such architechture is an array of possible object, each one with its predicted class, location in the image and abounding box. The loss function of SSD is a combination of a catagorical crossentropy and a mean square error (MSE) of the predicted location and bounding of each object.

Dependencies:

• install tensorflow (gpu is recomended)
• install opencv (pip install opencv-python --user)
• Clone the repository
• unzip ssd_300_vgg.ckpt file under the model folder.

[1] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC. Ssd: Single shot multibox detector. InEuropean conference on computer vision 2016 Oct 8 (pp. 21-37). Springer, Cham.