DMNet复现(二)之模型篇：Density map guided object detection in aerial image

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已于 2023-09-23 08:48:44 修改 · 971 阅读

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#目标检测 #人工智能 #计算机视觉

于 2023-09-19 18:25:51 首次发布

博客围绕目标检测展开，采用基于MMDetection的Swin Transformer Tiny框架进行模型训练，训练方法可参考官方教程。在融合检测方面，分别进行Global Image和Local Image检测，保存检测结果为json文件，最后对检测结果进行融合，不过实际效果未达论文水平，存在参数选择问题。

以前用Swin Transformer Tiny训练了40epoch的，官方用的Faster RCNN，这里先用Swin Transformer Tiny进行测试。

模型训练

采用基于MMDetection的框架Swin Transformer Tiny进行训练，训练方法可参考官方教程。

融合检测

Global Image 检测

这是我的配置文件及目录

需要用训练好的权重进行全局图像检测，将结果保存为.bbox.json文件。

环境信息：

sys.platform: linux
Python: 3.8.10 (default, Jun  4 2021, 15:09:15) [GCC 7.5.0]
CUDA available: True
numpy_random_seed: 2147483648
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 1.10.0+cu113
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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 11.3
  - 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 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -DEDGE_PROFILER_USE_KINETO -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=1.10.0, 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: 0.11.1+cu113
OpenCV: 4.6.0
MMEngine: 0.8.4
mmdetection: 2.11.0+461e003

采用以下配置文件

python tools/test.py  work_dirs/swin_s/swin_tiny_global_detection.py /root/autodl-tmp/result/swin_s/epoch_40.pth --eval bbox

# 继承之前的配置文件
_base_ = ["./mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py"]

# 修改数据路径
dataset_type = 'CocoDataset'
data_root = '/root/autodl-tmp/VisDrone2019/'

data = dict(
    samples_per_gpu=1,
    workers_per_gpu=0,
    train=dict(
        type=dataset_type,
        ann_file=data_root + 'VisDrone2019-DET-train/Global/train.json',
        img_prefix=data_root + 'VisDrone2019-DET-train/Global/images/',),
    val=dict(
        type=dataset_type,
        ann_file=data_root + 'VisDrone2019-DET-val/Global/val.json',
        img_prefix=data_root + 'VisDrone2019-DET-val/Global/images/',),
    test=dict(
        type=dataset_type,
        ann_file=data_root + 'VisDrone2019-DET-test-dev/Global/test.json',
        img_prefi