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 Transfer Learning Toolkit (TLT) to facilitate transfer learning.

- **YOLOV3**
- **YOLOV4**
- **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
- googlenet
- mobilenet_v1/mobilenet_v2
- squeezenet
- darknet19/darknet53
- efficientnet_b0
- cspdarknet19/cspdarknet53

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

+---------------------------+-------------------------------------------------------------------------------+
|                           | **Object Detection**                                                          |
+---------------------------+----------------+---------+------------+---------------+----------+------------+
| **Backbone**              | **FasterRCNN** | **SSD** | **YOLOv3** | **RetinaNet** | **DSSD** | **YOLOv4** |
+---------------------------+----------------+---------+------------+---------------+----------+------------+
|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        |
+---------------------------+----------------+---------+------------+---------------+----------+------------+
|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 :code:`all_projections` field to :code:`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`_.

.. _NGC catalog page: https://ngc.nvidia.com/catalog/models/nvidia:tlt_pretrained_object_detection