SSD_MobileNet

SSD: Single Shot MultiBox Detector | a PyTorch Model for Object Detection | VOC , COCO | Custom Object Detection

This repo contains code for Single Shot Multibox Detector (SSD) with custom backbone networks. The authors' original implementation can be found here.

Dataset

• Pascal Visual Object Classes (VOC) data from the years 2007 and 2012.
• COCO.
• Custom Dataset.

VOC dataset

VOC dataset contains images with twenty different types of objects.

{'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'}

Each image can contain one or more ground truth objects.

Each object is represented by –

• a bounding box in absolute boundary coordinates

• a label (one of the object types mentioned above)

• a perceived detection difficulty (either 0, meaning not difficult, or 1, meaning difficult)

Download

Specfically, you will need to download the following VOC datasets –

• 2007 trainval (460MB)

• 2012 trainval (2GB)

• 2007 test (451MB)

Inputs to model

We will need three inputs.

Images

• For SSD300 variant, the images would need to be sized at 300, 300 pixels and in the RGB format.
• PyTorch follows the NCHW convention, which means the channels dimension (C) must precede the size dimensions(1, 3, 300, 300).

Therefore, images fed to the model must be a Float tensor of dimensions N, 3, 300, 300, and must be normalized by the aforesaid mean and standard deviation. N is the batch size.

Objects' Bounding Boxes

For each image, the bounding boxes of the ground truth objects follows (x_min, y_min, x_max, y_max) format`.

Training

• In config.json change the paths.
• "backbone_network" : "MobileNetV2" or "MobileNetV1"
• For training run

  python train.py config.json
  

Inference

python inference.py image_path checkpoint