Yolo v7图像目标检测无标注问题排查求助

我在Docker中部署YOLOv7，程序运行无报错，但生成的检测图像和原图完全一致，没有任何目标标注。以下是完整复现步骤、控制台输出及环境信息，求排查问题。

复现命令 #

git clone https://github.com/WongKinYiu/yolov7
cd yolov7

nvidia-docker run --name yolov7 -it --rm -v "$CWD":/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3

# 容器内操作
cd /yolov7
python -m pip install virtualenv
python -m virtualenv venv3
. venv3/bin/activate
pip install -r requirements.txt
apt update
apt install -y zip htop screen libgl1-mesa-glx
pip install seaborn thop
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg

检测命令控制台输出 #

# python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg 
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', no_trace=False, nosave=False, project='runs/detect', save_conf=False, save_txt=False, source='inference/images/horses.jpg', update=False, view_img=False, weights=['yolov7.pt'])
YOLOR 🚀 v0.1-115-g072f76c torch 1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1650, 3903.875MB)

Fusing layers... 
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
Model Summary: 306 layers, 36905341 parameters, 6652669 gradients
 Convert model to Traced-model... 
 traced_script_module saved! 
 model is traced! 

/yolov7/venv3/lib/python3.8/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Done. (150.9ms) Inference, (0.3ms) NMS
 The image with the result is saved in: runs/detect/exp6/horses.jpg
Done. (0.616s)

按预期，生成的图像runs/detect/exp6/horses.jpg应显示检测结果，但它与原图inference/images/horses.jpg完全一致，无任何差异。

环境信息 #

NVIDIA驱动 #

$ nvidia-smi 
Tue Dec  6 09:47:03 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.60.11    Driver Version: 525.60.11    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+| 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  NVIDIA GeForce ...  On   | 00000000:01:00.0 Off |                  N/A || 45%   27C    P8    N/A /  75W |     13MiB /  4096MiB |      0%      Default ||                               |                      |                  N/A |+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  ||  GPU   GI   CI        PID   Type   Process name                  GPU Memory ||        ID   ID                                                   Usage      ||=============================================================================||    0   N/A  N/A      1152      G   /usr/lib/xorg/Xorg                  9MiB ||    0   N/A  N/A      1256      G   /usr/bin/gnome-shell                2MiB |+-----------------------------------------------------------------------------+

Ubuntu版本 #

$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description:    Ubuntu 20.04.4 LTS
Release:        20.04
Codename:       focal

排查建议 #

• 验证权重文件：确认yolov7.pt是否完整下载，可删除后重新下载官方权重文件替换，再运行检测。
• 降低置信度：当前设置的--conf 0.25可能过滤掉了所有检测结果，尝试改为--conf 0.1或更低阈值测试。
• 跳过虚拟环境：容器基础环境已预装PyTorch，可尝试不创建虚拟环境，直接安装依赖后运行检测，避免版本冲突。
• 检查测试图像：替换为其他清晰的目标测试图，确认原图像是否存在模糊、目标过小等问题导致检测失败。
• 指定推理设备：手动添加--device 0强制使用GPU，或--device cpu切换到CPU推理，排查设备兼容性问题。

内容的提问来源于stack exchange，提问作者丶 Limeー来夢 丶