Total Number of Parameters about lite-mono HOT 4 CLOSED

Hello,

There is a function that you can use to compute the parameters and FLOPs of a model. You need to install thop first.

from lite-mono.

Hello,

There is a function that you can use to compute the parameters and FLOPs of a model. You need to install thop first.

Thank you for your prompt response.
I have an additional question regarding training the lite-mono-8m model from scratch. I'm unsure whether I need to include "--model lite-mono-8m" in my command line. Could you please clarify which of the following commands should be used for training the lite-mono-8m model from scratch with an image size of 1024x320?
Command 1: "python train.py --data_path /media/armin/DATA/Lightweight/kitti_data --model_name mytrain --model lite-mono-8m --num_epochs 30 --num_workers 4 --batch_size 4 --height 320 --width 1024"
or
Command 2: "python train.py --data_path /media/armin/DATA/Lightweight/kitti_data --model_name mytrain --num_epochs 30 --num_workers 4 --batch_size 4 --height 320 --width 1024"

In addition, I would like to train my model using an image resolution of 1024x320, using the pre-trained ImageNet model you provided in this repository. However, the pre-trained ImageNet model available is specifically trained for an image resolution of 640x192. I'm wondering if it is possible to use the same pre-trained model for different image sizes, or if I need to create my own pre-trained model specifically for an image resolution of 1024x320.

from lite-mono.

1. Command 1 is correct. If you do not specify --model the default model is lite-mono.
2. You don't need different ImageNet pre-trained weights for the resolution of 1024x320. The pre-trained weights were obtained by training on ImageNet using an input size of 256x256.

from lite-mono.

1. Command 1 is correct. If you do not specify --model the default model is lite-mono.
2. You don't need different ImageNet pre-trained weights for the resolution of 1024x320. The pre-trained weights were obtained by training on ImageNet using an input size of 256x256.

Thank you so much!

from lite-mono.