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.
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.