Comments (4)
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.
- Command 1 is correct. If you do not specify
--model
the default model islite-mono
. - 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 of256x256
.
from lite-mono.
- Command 1 is correct. If you do not specify
--model
the default model islite-mono
.- 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 of256x256
.
Thank you so much!
from lite-mono.
Related Issues (20)
- Hi,I've got some new questions HOT 9
- Some question about the result HOT 4
- The edges of the image are foggy and blurry during the training process HOT 4
- Inexplicable "No such file or directory: 'Lite-Mono-main\\kitti_data\\\2011_09_26/2011_09_26_drive_0002_sync\\\image_02/data\\- 000000001.png'" HOT 2
- the CPU utilization has been very high, but the GPU utilization has been very low HOT 2
- training with Nuscenes dataset HOT 39
- About indicator sq_rel HOT 6
- Test outputs is black HOT 8
- I read your paper “For models trained from scratch an initial learning rate of 5e−4 with a cosine learning rate schedule [26] is adopted” But how should I implement it in the code? HOT 4
- How the reproduction of the results of the model achieves the results in the paper HOT 6
- Hello, my reproduction code is exactly the same as the code uploaded by the author of the paper, CUDA11.0, PyTorch 1.7.1. Please check your code and replace your CUDA and pytorch version.
- model reproduction HOT 11
- Hello, I have some questions regarding the model training with the KITTI dataset HOT 4
- Pre-training Weights HOT 1
- about pretrain acc HOT 1
- the results of training are black pictures HOT 5
- test code for pose HOT 2
- Subsequent issues with gt_depths.npz HOT 4
- sorry to bothering you HOT 1
- Output resolution problem HOT 5
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