Fast and accurate object detection with end-to-end GPU optimization
This release adds:
MobileNetV2FPN
backboneodtk export model.pth model.plan --rotated-bbox
--rotated-bbox
flag is automatically applied when running odtk infer or odtk export on a model trained with ODTK version 0.2.2 or later.--dynamic-batch-opts
option to odtk export
.
Version 0.2.0 introduces rotated detections.
train arguments
:
--rotated-bbox
: Trains a model is predict rotated bounding boxes [x, y, w, h, theta]
instead of axis aligned boxes [x, y, w, h]
.infer arguments
:
--rotated-bbox
: Infer a rotated model.The project has reverted to the name Object Detection Toolkit (ODTK), to better reflect the multi-network nature of the repo.
retinanet
has been replaced with odtk
. All subcommands remain the same.--rotated-bbox
flag cannot be exported to ONNX or a TensorRT Engine.Warning 1: NCCL watchdog
[E ProcessGroupNCCL.cpp:284] NCCL watchdog thread terminated
Warning 2: Save state warning
/opt/conda/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:201: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler.
warnings.warn(SAVE_STATE_WARNING, UserWarning)
This release adds image augmentation (brightness, contrast, hue, saturation) and four degree rotational augmentation.
Added parameters:
--augment-rotate
: Randomly rotates the training images by 0°, 90°, 180° or 270°.--augment-brightness
(float): Randomly adjusts brightness of image. The value sets the standard deviation of a Gaussian distribution. The degree of augmentation is selected from this distribution. Default: 0.05--augment-contrast
(float): Randomly adjusts contrast of image. The value sets the standard deviation of a Gaussian distribution. The degree of augmentation is selected from this distribution. Default: 0.05--augment-hue
(float): Randomly adjusts hue of image. The value sets the standard deviation of a Gaussian distribution. The degree of augmentation is selected from this distribution. Default: 0.01--augment-saturation
(float): Randomly adjusts saturation of image. The value sets the standard deviation of a Gaussian distribution. The degree of augmentation is selected from this distribution. Default: 0.05--regularization-l2
(float): Sets the L2 regularization of the optimizer. Default: 0.0001This pre-release, corresponding with the NVIDIA GPU Cloud (NGC) PyTorch 19.04 container version, includes the first iteration of pretrained RetinaNet models created with this project: