在WiderFace上训练的mAP为0

[Cathy1900]

Environment info：
sys.platform: linux
Python: 3.7.13 (default, Oct 18 2022, 18:57:03) [GCC 11.2.0]
CUDA available: True
GPU 0,1: NVIDIA A100 80GB PCIe
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.1, V11.1.74
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 1.10.0
PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 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
  TorchVision: 0.11.0
  OpenCV: 4.6.0
  MMCV: 1.6.2
  MMCV Compiler: GCC 9.3
  MMCV CUDA Compiler: 11.3
  MMDetection: 2.25.3+unknown

---

您好，我在mmdetection上根据您论文里Implementation Details的介绍（关于训练widerface），对ddod的优化器部分进行了以下改动：
optimizer = dict(type='SGD', lr=0.0075, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='CosineRestart',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.1,
periods=[
30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,
30, 30
],
restart_weights=[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
],
min_lr_ratio=0.01)
runner = dict(type='EpochBasedRunner', max_epochs=600)
此外，我也将reg loss改为了DIoU loss，但是每个epoch报告的mAP均为0。请问是什么情况呢？

[zehuichen123]

Hi, Thanks for your attention. Currently, we have no plans for supporting WiderFace, since the codebase for WiderFace is mainly based on TinaFace, which is different from the current one. For more details, you may refer to #7, #8.