PaddlePaddle / PaddlePaddle/FastDeploy
PPYOLOER使用C++推理（fastdeploy-win-x64-gpu-1.0.7）获取目标结果不正确
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Description
auto option = fastdeploy::RuntimeOption();
option.UseGpu();
auto model = fastdeploy::vision::detection::PPYOLOER(model_file, params_file,
config_file, option);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
return;
}
const cv::Mat im = cv::imread(image_file);
fastdeploy::vision::DetectionResult res;
if (!model.Predict(im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
使用事例中的网络和图片，获取res.rotated_boxes.size() =0
请帮忙分析一下是哪里问题？
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Research direction
Start by reproducing the C++ PPYOLOER example with fastdeploy-win-x64-gpu-1.0.7, the example network, and the supplied image. Trace the PPYOLOER Predict entry point and inspect how the result is populated, especially res.rotated_boxes. Done means identifying why the rotated-box result is empty and documenting a verified correction or limitation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, opencv
- Domain
- computer-vision
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100