Setting Environment

Requirement of OS and Graphics card

OS: Ubuntu 18.04 (I used Windows 10 WSL version)

CUDA 10.1 supports 14.04 ~ 18.10.
And it looks like CUDA 11.1 only supports 16.04 ~ 20.04.
In my experience, 22.04 was not possible. 16.04 ~ 20.04 is the expected range of possibilities, but I highly recommend going with 18.04.

Graphics Card (=VGA)

Nivida GeForce GTX 1060 Ti (compute capability = 7.5)
Nvidia GeForce RTX 3080 Mobile (compute capability = 8.6)
Please refer site_link.
Presumably, to be compatible with CUDA 10.1 ~ 11.1, you need to use a graphics card with a compute capability of 3.0 ~ 8.6.
The two graphics cards above are the ones I've had success emplementing TransFusion.
Below I'll show you how to set up your environment for TransFusion based on two types of your graphics card.

Details

First you need to determine what type of graphics card you have. See the link above.

Type 1. Nvidia GeForce GTX 1060 Ti (& Those with a compute capability of 3.0 to 7.5, so CUDA 10.1 is compatible, probably.)
Type 2. Nvidia GeForce RTX 3080 Mobile (& Those with a compute capability of 3.5 to 8.6, so CUDA 11.1 is compatible, probably.)

(1) Install Python==3.7.5 on Ubuntu.

sudo apt update
sudo apt install software-properties-common
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.7.5

(2) Install pip and virtualenv.

sudo apt install python3-pip
python3.7 -m pip install virtualenv

(3) Create a local directory and a virtual environment.

mkdir [your directory name] # example: project # example path: /home/chanju/project
cd [your directory name] 
virtualenv [your virtual environment name] --python=python3.7 # example: venv

# In the code below, I'll use the names I've given as examples, so check them carefully.

(4) Make a alias of activating virtual environment.

cd .. # go to the parent directory # example: chanju
nano .bashrc # If you don't have the nano editor, use another editor.

# bash
alias activate='activate /home/chanju/project/venv/bin/activate; cd /home/chanju/project' # Add to end.

source .bashrc
activate

(5) Download cuda and cudnn.

If you have type1

Download cuda-10.1

# Download .run file to click download button.
sudo sh cuda_10.1.105_418.39_linux.run

Download cudnn-7.6.5

# You need login for nvidia.

# Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.1 --> cuDNN Library for Linux

tar -zxvf cudnn-10.1-linux-x64-v7.6.5.32.tgz

sudo cp cuda/include/cudnn*.h /usr/local/cuda-10.1/include 
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-10.1/lib64 
sudo chmod a+r /usr/local/cuda-10.1/include/cudnn*.h /usr/local/cuda-10.1/lib64/libcudnn*

# Make path for CUDA.
cd ~ # go to chanju repo, for example.
nano .bashrc

# bash
# Add to end
export PATH=/usr/local/cuda-10.1/bin:$PATH
export LC_LIBRARY_PATH=/usr/local/cuda-10.1/lib64:$LC_LIBRARY_PATH
export LC_LIBRARY_PATH=/usr/local/cuda-10.1/extras/CUPTI/lib64:$LC_LIBRARY_PATH

source .bashrc
activate

If you have type2

Download cuda-11.1

# I have tried various versions such as 11.1.0, 11.1.0 update1, etc.
# In a successful environment, I only checked the version below, so I highly recommend below version.

wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run
sudo sh cuda_11.1.0_455.23.05_linux.run

Download cudnn-8.0.5

# I have tried two versions as 8.0.4, 8.0.5.
# In a successful environment, I only checked the 8.0.5 version, so I highly recommend 8.0.5 version.

# You need login for nvidia.

# Download cuDNN v8.0.5 (November 9th, 2020), for CUDA 11.1 --> cuDNN Library for Linux (x86_64)

tar -zxvf cudnn-11.1-linux-x64-v8.0.5.39.tgz

sudo cp cuda/include/cudnn*.h /usr/local/cuda-11.1/include 
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-11.1/lib64 
sudo chmod a+r /usr/local/cuda-11.1/include/cudnn*.h /usr/local/cuda-11.1/lib64/libcudnn*

# Make path for CUDA.
cd ~ # go to chanju repo, for example.
nano .bashrc

# bash
# Add to end
export PATH=/usr/local/cuda-11.1/bin:$PATH
export LC_LIBRARY_PATH=/usr/local/cuda-11.1/lib64:$LC_LIBRARY_PATH
export LC_LIBRARY_PATH=/usr/local/cuda-11.1/extras/CUPTI/lib64:$LC_LIBRARY_PATH

source .bashrc
activate

(6) Download torch and torchvision

See pytorch pevious version page.

If you have type 1

pip install torch==1.6.0+cu101 torchvision==0.8.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html

If you have type 2

# I have tried various versions such as 1.8.0, 1.8.1 and 1.9.0. Only 1.9.0 succeeded.
# The other two versions showed the following error on train.py. "RuntimeError: CUDA error: no kernel image is available for execution on the device"
# The above error seems to be a chronic problem that many people experience and could only be solved by upgrading the pytorch to 1.9.0 and reinstalling and building mmcv, mmdet, and mmdet3d.

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

(7) Download the required packages in advance.

Before we download mmcv and build mmdet and TransFusion(=mmdet3d), we need to download these.

The versions of these packages relate to the March 2021 when mmdet3d 0.11.0 ~ 0.12.0 is released from which TransFusion was forked.

pip install cython==0.29.33 setuptools==50.3.2 ninja==1.10.0post2 yapf==0.31.0 cmake==3.18.4post1 numba==0.48.0 llvmlite==0.31.0 numpy==1.19.5

# or cmake==3.13.0, setuptools==52.0.0
# If you need, open3d==0.17.0, protobuf==3.20.3,  

sudo apt install python3.7-dev
pip install pycocotools==2.0.2

Download spconv-1.2.1.

git clone --branch v1.2.1 https://github.com/traveller59/spconv.git # clone v1.2.1 branch to your venv/lib/python3.7/site-packages
python ~/project/venv/lib/python3.7/site-packages/spconv-1.2.1/setup.py bdist_wheel # Don't use python setup.py install

(8) Download mmcv-full.

If you have type 1

# https://download.openmmlab.com/mmcv/dist/index.html
# https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
# Refer above two site, plz.

pip install mmcv-full==1.2.4 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html

If you have type 2

# https://download.openmmlab.com/mmcv/dist/index.html
# https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
# Refer above two site, plz.

pip install mmcv-full==1.3.10 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html

(9) Build mmdet.

Download tar.gz file of v2.10.0

# https://mmdetection.readthedocs.io/en/v2.15.0/get_started.html
# https://github.com/open-mmlab/mmdetection/releases/tag/v2.10.0
# Refer above two site, plz.

tar -zxvf mmdetection-2.10.0.tar.gz # in anywhere, maybe in your project directory
cd mmdetection-2.10.0
python setup.py develop

cd ..

# and move mmdet directory in mmdetection-2.10.0 to venv/lib/python3.7/site-packages

(10) Build TransFusion!

If you have type1

git clone https://github.com/XuyangBai/TransFusion.git # in your project directory
cd TransFusion
python setup.py develop

cd ..

If you have type2

The commit which 'Transfusion' forked from is 7c30072: "[Fixed] modify vote_head to support 3dssd (#396)".

I merged the transfusion with the fourth commit(ID: d055876 / "update version file") right after it.
- It was possible to merge without affecting the implement of TransFusion for the following reasons.
  - The first commit right after: only changes the comments for the arguments.
  - The second commit right after: only changes them non-related with implement codes.
  - The third commit right after: fixes the problem between torch-1.8.0 and CUDA compiler(include 11.1).
  - The fourth commit right after: only changees version of version file.
```
git clone https://github.com/superego18/TransFusion-ChanJu.git # in your project directory
cd TransFusion-ChanJu
python setup.py develop

cd ..
```

Make path for mmdet3d in TransFusion directory

cd ~ # go to the parent directory # example: chanju
nano .bashrc # If you don't have the nano editor, use another editor.

# bash
export PYTHONPATH=/home/{your_name}/{your_repo_name}/{TransFusion_repo_name}:/home/{your_name}/{your_repo_name}/venv/lib:$PYTHONPATH' # Add to end.

source .bashrc
activate

(12) Additional error fixes for type2.

If you have type 2
- Make pycocotools compatiable with mmdet. (When error is occured)
  - Download pycocotools again using a different method.
```
pip uninstall pycocotools
pip install pycocotools==2.0.2 --no-cache-dir --no-binary :all:
```
  - Fix the assert version of pycocotools in mmdetection.
```
# venv/lib/python3.7/site-packages/mmdetection-2.10.0/mmdet/datasets/coco.py", line 21
assert pycocotools.__version__ >= '2.0.2' # (12.0.2 --> 2.0.2)
```
  - Add version in pycocotools.
```
# venv/lib/python3.7/site-packages/pycocotools/init.py"
__version__ = '2.0.2' (add on line 2)
```
  - Then you can run create_data.py
- Train by nuscenes dataset
  - When you run train.py on nuscenes, maybe you will encounter the ouput "do not find nuscenes_gt_database ~~"
  - So you move only nuscenes_gt_database directory in data/nuscenes to your TransFusion directory.
  - I think the cause is probably due to the version difference of mmcv.
  - If you add print(filepath) to the def get(self, filepath) of fileio/file_client.py in mmcv, only the paths of files corresponding to nuscenes_gt_database come out differently.

Presumably this will allow you to implement the demo code in getting_started.md and codes in data_preparation.md, train.py, etc.

My environment info

When I run torch's collect_env code below

activate

python -m torch.utils.collect_env

My type 1 shows

PyTorch version: 1.6.0+cu101
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 18.04.6 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.13.0

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti
Nvidia driver version: 517.00
cuDNN version: /usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.5

Versions of relevant libraries:
[pip3] numpy==1.19.5
[pip3] torch==1.6.0+cu101
[pip3] torchvision==0.7.0+cu101
[conda] Could not collect

My type 2 shows

PyTorch version: 1.9.0+cu111
Is debug build: False
CUDA used to build PyTorch: 11.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.13.0
Libc version: glibc-2.26

Python version: 3.7 (64-bit runtime)
Python platform: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-Ubuntu-18.04-bionic
Is CUDA available: True
CUDA runtime version: 11.1.74
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU
Nvidia driver version: 528.49
cuDNN version: Probably one of the following:
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.19.5
[pip3] torch==1.9.0+cu111
[pip3] torchvision==0.10.0+cu111
[conda] Could not collect

When I run train.py and I can see my env_info

My type 1 shows

sys.platform: linux
Python: 3.7.5 (default, Dec  9 2021, 17:04:37) [GCC 8.4.0]
CUDA available: True
GPU 0: NVIDIA GeForce GTX 1660 Ti
CUDA_HOME: /usr/local/cuda-10.1
NVCC: Cuda compilation tools, release 10.1, V10.1.243
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.6.0+cu101
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2019.0.5 Product Build 20190808 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
  - CuDNN 7.6.3
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.7.0+cu101
OpenCV: 4.9.0
MMCV: 1.2.4
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
MMDetection: 2.10.0
MMDetection3D: 0.11.0+

My type 2 shows

2024-01-11 05:53:50,299 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.5 (default, Dec  9 2021, 17:04:37) [GCC 8.4.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU
CUDA_HOME: /usr/local/cuda-11.1
NVCC: Build cuda_11.1.TC455_06.29069683_0
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.9.0+cu111
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.10.0+cu111
OpenCV: 4.9.0
MMCV: 1.3.10
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.1
MMDetection: 2.10.0
MMDetection3D: 0.12.0+e8cc677

TransFusion repository

PyTorch implementation of TransFusion for CVPR'2022 paper "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers", by Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu and Chiew-Lan Tai.

This paper focus on LiDAR-camera fusion for 3D object detection. If you find this project useful, please cite:

@article{bai2021pointdsc,
  title={{TransFusion}: {R}obust {L}iDAR-{C}amera {F}usion for {3}D {O}bject {D}etection with {T}ransformers},
  author={Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu and Chiew-Lan Tai},
  journal={CVPR},
  year={2022}
}

Introduction

LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against inferior image conditions, e.g., bad illumination and sensor misalignment, is under-explored. Existing fusion methods are easily affected by such conditions, mainly due to a hard association of LiDAR points and image pixels, established by calibration matrices. We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. Specifically, our TransFusion consists of convolutional backbones and a detection head based on a transformer decoder. The first layer of the decoder predicts initial bounding boxes from a LiDAR point cloud using a sparse set of object queries, and its second decoder layer adaptively fuses the object queries with useful image features, leveraging both spatial and contextual relationships. The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy. We additionally design an image-guided query initialization strategy to deal with objects that are difficult to detect in point clouds. TransFusion achieves state-of-the-art performance on large-scale datasets. We provide extensive experiments to demonstrate its robustness against degenerated image quality and calibration errors. We also extend the proposed method to the 3D tracking task and achieve the 1st place in the leaderboard of nuScenes tracking, showing its effectiveness and generalization capability.

updates

March 23, 2022: paper link added
March 15, 2022: initial release

Main Results

Detailed results can be found in nuscenes.md and waymo.md. Configuration files and guidance to reproduce these results are all included in configs, we are not going to release the pretrained models due to the policy of Huawei IAS BU.

nuScenes detection test

Model	Backbone	mAP	NDS	Link
TransFusion-L	VoxelNet	65.52	70.23	Detection
TransFusion	VoxelNet	68.90	71.68	Detection

nuScenes tracking test

Model	Backbone	AMOTA	AMOTP	Link
TransFusion-L	VoxelNet	0.686	0.529	Detection / Tracking
TransFusion	VoxelNet	0.718	0.551	Detection / Tracking

waymo detection validation

Model	Backbone	Veh_L2	Ped_L2	Cyc_L2	MAPH
TransFusion-L	VoxelNet	65.07	63.70	65.97	64.91
TransFusion	VoxelNet	65.11	64.02	67.40	65.51

Use TransFusion

Installation

Please refer to getting_started.md for installation of mmdet3d. We use mmdet 2.10.0 and mmcv 1.2.4 for this project.

Benchmark Evaluation and Training

Please refer to data_preparation.md to prepare the data. Then follow the instruction there to train our model. All detection configurations are included in configs.

Note that if you a the newer version of mmdet3d to prepare the meta file for nuScenes and then train/eval the TransFusion, it will have a wrong mAOE and mASE because mmdet3d has a coordinate system refactoring which affect the definitation of yaw angle and object size (l, w).

Acknowlegement

We sincerely thank the authors of mmdetection3d, CenterPoint, GroupFree3D for open sourcing their methods.

Name		Name	Last commit message	Last commit date
.dev_scripts		.dev_scripts
.github		.github
configs		configs
demo		demo
docker		docker
docs		docs
mmdet3d		mmdet3d
requirements		requirements
resources		resources
tests		tests
tools		tools
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.readthedocs.yml		.readthedocs.yml
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
README_zh-CN.md		README_zh-CN.md
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py

Navigation Menu

Repository files navigation

Setting Environment

Requirement of OS and Graphics card

OS: Ubuntu 18.04 (I used Windows 10 WSL version)

Graphics Card (=VGA)

Details

My environment info

TransFusion repository

Introduction

Main Results

nuScenes detection test

nuScenes tracking test

waymo detection validation

Use TransFusion

Acknowlegement

About

Releases

Packages

Used by

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Setting Environment

Requirement of OS and Graphics card

OS: Ubuntu 18.04 (I used Windows 10 WSL version)

Graphics Card (=VGA)

Details

My environment info

TransFusion repository

Introduction

Main Results

nuScenes detection test

nuScenes tracking test

waymo detection validation

Use TransFusion

Acknowlegement

About

Resources

Code of conduct

Contributing

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages