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Models

Source

IceVision offers a large number of models by supporting the following Object Detection Libraries:

You will enjoy using our unified API while having access to a large repertoire of SOTA models. Switching models is as easy as changing one word. There is no need to be familiar with all the quirks that new models and implementations introduce.

Creating a model

In order to create a model, we need to: * Choose one of the libraries supported by IceVision * Choose one of the models supported by the library * Choose one of the backbones corresponding to a chosen model

You can access any supported models by following the IceVision unified API, use code completion to explore the available models for each library.

Selecting a model only takes two simple lines of code. Check out the following examples illustrating some of the models libraries we support:

MMDetection

model_type = models.mmdet.retinanet
backbone = model_type.backbones.resnet50_fpn_1x(pretrained=True)
# Instantiate the mdoel
model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map))

Torchvision

model_type = models.torchvision.retinanet
backbone = model_type.backbones.resnet50_fpn
# Instantiate the mdoel
model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map))

EfficientDet

model_type = models.ross.efficientdet
backbone = model_type.backbones.tf_lite0
# The efficientdet model requires an img_size parameter
# Instantiate the mdoel
model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), img_size=img_size)

YOLOv5

model_type = models.ultralytics.yolov5
backbone = model_type.backbones.small
# The yolov5 model requires an img_size parameter
# Instantiate the mdoel
model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), img_size=img_size)

As pretrained models are used by default, we typically leave this out.