SSD_googleimage_challenge2019

Model Description

This SSD300 model is based on the SSD: Single Shot MultiBox Detector paper, which describes SSD as “a method for detecting objects in images using a single deep neural network”. The input size is fixed to 300x300.paper link

Additional enhancement:

• The conv5_x, avgpool, fc and softmax layers were removed from the original classification model.
• All strides in conv4_x are set to 1x1.

The backbone is followed by 5 additional convolutional layers. In addition to the convolutional layers, we attached 6 detection heads:

• The first detection head is attached to the last conv4_x layer.
• The other five detection heads are attached to the corresponding 5 additional layers.

The main difference between this model and the one described in the paper is in the backbone. Specifically, the VGG model is obsolete and is replaced by the ResNet-50 model.

Reference: https://pytorch.org/hub/nvidia_deeplearningexamples_ssd/¶

Dataset: https://www.kaggle.com/c/open-images-2019-object-detection

Result: