Comments (8)
Hi, can you show some images? Are they shown correctly during training in the tensorboard?
from lite-mono.
Sorry for the late reply. I would like to upload the training results, but there have been issues with GitHub's push.
This is the opt.json for the inference results and training weights.
from lite-mono.
{
"data_path": "/workspace/IPC_Indoor_20231030_1530_P",
"log_dir": "/mnt/smb-149/02_Tasks/01_Depth_Estimation/Lite-Mono-main/tmp",
"model_name": "mytrain-010-lite-mono-8m_640_192-resnet50-separate_resnet",
"split": "odom",
"split_num": 3,
"model": "lite-mono-8m",
"weight_decay": 0.01,
"drop_path": 0.2,
"num_layers": 50,
"dataset": "lift_odom",
"png": false,
"height": 192,
"width": 640,
"disparity_smoothness": 0.001,
"scales": [
0,
1,
2
],
"min_depth": 0.1,
"max_depth": 100.0,
"use_stereo": false,
"frame_ids": [
0,
-1,
1
],
"profile": true,
"batch_size": 12,
"lr": [
0.001,
5e-06,
31.0,
0.001,
5e-06,
31.0
],
"num_epochs": 30,
"scheduler_step_size": 30,
"v1_multiscale": false,
"avg_reprojection": false,
"disable_automasking": false,
"predictive_mask": false,
"no_ssim": false,
"mypretrain": null,
"weights_init": "pretrained",
"pose_model_input": "pairs",
"pose_model_type": "separate_resnet",
"no_cuda": false,
"num_workers": 12,
"load_weights_folder": "pretrain_weights/lite-mono-8m_640_192/",
"models_to_load": [
"encoder",
"depth"
],
"log_frequency": 10,
"save_frequency": 3,
"disable_median_scaling": false,
"pred_depth_scale_factor": 1,
"ext_disp_to_eval": null,
"eval_split": "eigen",
"save_pred_disps": false,
"no_eval": false,
"eval_out_dir": null,
"post_process": false
}
from lite-mono.
Since the depth range of your image is small, so you need to change max_depth
in the configuration. The most important thing is to change the camera intrinsics in the dataset class.
from lite-mono.
Thank you for your help!! And I have other questions:
(1) What is the unit of this parameter max_depth
, centimeters or meters?
(2) How can I obtain KITTIDataset(). K
from the camera?
from lite-mono.
- meter
- If you collect your dataset using your own camera, then you need to calibrate your camera. There are a lot of documentations on camera calibration.
from lite-mono.
Okay, thank you very much! I'll train it again.
from lite-mono.
I am now closing this issue as there is no further update.
from lite-mono.
Related Issues (20)
- Training on Mid-Air Dataset HOT 7
- How the reproduction of the results of the model achieves the results in the paper HOT 2
- About the CDC module and LGFI module HOT 8
- 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
- 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
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from lite-mono.