We have trained a YOLOv4 model with TAO, successfully exported it to .etlt and then to tensorRT .engine. We can perform inference with the models successfully.
We have set augmentation_config in training configuration as follows:
augmentation_config {
hue: 0.1
saturation: 1.5
exposure: 1.5
vertical_flip: 0
horizontal_flip: 0.5
jitter: 0.3
output_width: 416
output_height: 416
output_channel: 3
randomize_input_shape_period: 10
mosaic_prob: 0.5
mosaic_min_ratio: 0.2
so when we export the model, it is exported with dimensions -1x3x416x416. My question is, is it possible to export the model with different output_width x output_height than the one it was trained with (ex: -1x3x608x608) given we have set randomize_input_shape_period to a non-zero value.
In other words, what I am asking is setting randomize_input_shape_period equivalent to setting random=1 configuration in Darknet YOLO? .For example, with this setting, in Darknet YOLO, if the model was trained for width x height = 416x416, we can load this model as model with width x height = 608x608 and get better accuracies, or can load weights with lower width x height and will receiver faster inference, but with a reduction in accuracy.
Information
• Network Type - Yolo_v4
The batch interval to randomly change the output width and height. For value K, the augmentation pipeline will adjust output shape per K batches, and the adjusted output width/height will be within 0.6 to 1.5 times of the base width/height.