FYI.
Maybe…you can try with the following steps.
#1.set system environment variables
SET DISTUTILS_USE_SDK=1
call “D:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvars64.bat”
#2.make from source
git clone GitHub - facebookresearch/detectron2: Detectron2 is FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks. 6
cd detectron2
python setup.py build develop
d:_DL\detectron2>python
Python 3.8.10 (tags/v3.8.10:3d8993a, May  3 2021, 11:48:03) [MSC v.1928 64 bit (AMD64)] on win32
Type “help”, “copyright”, “credits” or “license” for more information.



import detectron2
detectron2.version
‘0.4.1’



#3.
D:_DL\detectron2>wget -nc -q https://github.com/facebookresearch/detectron2/raw/master/detectron2/utils/collect_env.py 2 && python collect_env.py

sys.platform            win32
Python                  3.8.10 (tags/v3.8.10:3d8993a, May  3 2021, 11:48:03) [MSC v.1928 64 bit (AMD64)]
numpy                   1.19.2
detectron2              0.4.1 @D:_DL\detectron2\detectron2
Compiler                MSVC 192930038
CUDA compiler           CUDA 11.0
detectron2 arch flags   D:_DL\detectron2\detectron2_C.cp38-win_amd64.pyd; cannot find cuobjdump
DETECTRON2_ENV_MODULE   
PyTorch                 1.9.0+cu111 @D:\Program Files (x86)\Python\Python38\lib\site-packages\torch
PyTorch debug build     False
GPU available           True
GPU 0                   Quadro T2000 with Max-Q Design (arch=7.5)
CUDA_HOME               D:\Program Files (x86)\NVIDIA GPU Computing Toolkit\CUDA\v11.0
Pillow                  8.2.0
torchvision             0.10.0+cu111 @D:\Program Files (x86)\Python\Python38\lib\site-packages\torchvision
torchvision arch flags  D:\Program Files (x86)\Python\Python38\lib\site-packages\torchvision_C.pyd; cannot find cuobjdump
fvcore                  0.1.5.post20210630
iopath                  0.1.8
cv2                     4.5.2

PyTorch built with:

C++ Version: 199711
MSVC 192829337
Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
OpenMP 2019
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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37
CuDNN 8.0.5
Magma 2.5.4
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=C:/w/b/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/w/b/windows/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON,