We have released the DANGER Dataset publicly to aid the research community in making advancements in model robustness and explainability.
The DANGER framework is universal to many opensource dataset. In this repo we provided two datasets - the vKITTI1 add-on dataset and the vKITTI2 add-on dataset.
[2021-06-09] DANGER
v0.1.0 is released.
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- Linux
- Python 3.6+
- PyTorch 0.4
- NVIDIA GPU (GPU memory > 8GB) + CUDA 9.0
- Conda
Use our notebook hosted on Colab/Colab Pro will help you setup all the prerequisites automatically.
Sun Jun 5 01:15:01 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |
| N/A 37C P0 28W / 250W | 0MiB / 16280MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Python 3.6.10 :: Anaconda, Inc.
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176
time: 2min 19s (started: 2022-06-05 01:12:42 +00:00)
We host our tutorial notebooks on Colab, and we suggest you initialize your vm with a GPU when you setup the 3D-SDN environment.
sudo chmod 755 ./DANGER/install.sh
./DANGER/install.sh
3D-SDN environment:
- There are sections in this notebook:
Hardware Setup
,3D-SDN Example
,Semantic Training
,Geometry Training
,Textural Training
, andtest.json
- Please run
Hardware Setup
to modify the default supported drivers that Colab provided. - Run
3D-SDN Example
to verify the installation, which would help you finish theGetting Started
section described in 3D-SDN. - Use
test.json
, provided in step 2, to generate your dataset. You can ignore theSemantic Training
,Geometry Training
,Textural Training
steps, but it would be helpful when you interested to train you own model.
Generated JSON
files are preserved at JSONs.
DANGER
is released under the MIT License
DANGER
is an open source project for dataset manipulation and model robustness test.
We would like to thank the authors of 3D-SDN
for their open-source release.
If you find this project useful in your research, please consider citing:
@article{DANGER,
title={},
author={A and B},
journal = {Neurips},
year={2022}
}
git add .
git commit -m 'init'
git branch -M main
git push -u origin main
- Calculate
u,v
- Verify
u,v
by check each operation invkitti_edit_benchmark.json
- Try generate
*.json
by generatedelete
operation - Try generate
*.json
by generatery
operation - Try generate
*.json
by changeu,v
operation