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.
The following backbones are supported with these detection networks:
resnet10/resnet18/resnet34/resnet50/resnet101
vgg16/vgg19
googlenet
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 | ||||||
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_projectionsfield toFalsein 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.