# Open Images Pre-trained Object Detection

Object detection is a popular computer vision technique that can detect one or multiple objects in a frame. Object detection will recognize the individual objects in an image and places bounding boxes around the object. This model object contains pretrained weights that may be used as a starting point with the following object detection networks in TAO Toolkit to facilitate transfer learning.

• YOLOv3

• YOLOv4

• YOLOv4-tiny

• FasterRCNN

• SSD

• DSSD

• RetinaNet

It is trained on a subset of the Google OpenImages dataset.

## Supported Backbones

The following backbones are supported with these detection networks:

• resnet10/resnet18/resnet34/resnet50/resnet101

• vgg16/vgg19

• mobilenet_v1/mobilenet_v2

• squeezenet

• darknet19/darknet53

• efficientnet_b0

• cspdarknet19/cspdarknet53

• cspdarknet_tiny

Some combinations might not be supported. See the matrix below for all supported combinations.

 Object Detection Backbone FasterRCNN SSD YOLOv3 RetinaNet DSSD YOLOv4 YOLOv4-tiny ResNet10/18/34/50/101 Yes Yes Yes Yes Yes Yes VGG 16/19 Yes Yes Yes Yes Yes Yes GoogLeNet Yes Yes Yes Yes Yes Yes MobileNet V1/V2 Yes Yes Yes Yes Yes Yes SqueezeNet Yes Yes Yes Yes Yes DarkNet 19/53 Yes Yes Yes Yes Yes Yes CSPDarkNet 19/53 Yes CSPDarkNet-tiny Yes Efficientnet B0 Yes Yes Yes Yes Efficientnet B1 Yes
Note
• These are unpruned models with just the feature extractor weights, and may not be used without retraining to deploy in a classification application.

• Please make sure to set the all_projections field to False in the spec file when training a ResNet101 model.

For more instructions on downloading and using the models defined here, refer to the NGC catalog page.