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Home Page: https://nndeploy-zh.readthedocs.io/zh/latest/
License: Apache License 2.0
nndeploy是一款模型端到端部署框架。以多端推理以及基于有向无环图模型部署为基础,致力为用户提供跨平台、简单易用、高性能的模型部署体验。
Home Page: https://nndeploy-zh.readthedocs.io/zh/latest/
License: Apache License 2.0
比如,不需要 自己cp config.cmake 做修改,也不用 clone 内容到本地,
本地放一张图片和一个小模型,可以一个shell 跑起来?
或者,可以一个shell 拉取下来 模型和图片?不需要在 clone了
看到源码里边有half头文件了,但貌似没有使用,请问下是否已经支持half推理?
您好,请问下可以支持下旋转目标检测的C++部署吗?例如这个yolov8的
目前有部署大语言模型的demo吗?
Hi!
num_classes and input_size for model should be available as model init parameters, because there can be multiple custom trained models of the same type (ex Yolov*) in single project, with different input size and num_classes. It will prevent code duplication.
Add YOLOv6 model for RKNN, for now, its most performant NN model for object detection for Rockchip. But RKNN-optimized YOLOv6 model has different opt_head that significately improves inference time (look at: https://github.com/airockchip/YOLOv6/commit/aa12cf70ee1795d00538f78a8857efd0cf17f7e4
Add RTSP (live Video) codec, it's almost the same as Video, except it takes not a Path but rtsp url
代码中,CMakelist里有很多hardcoded 的路径。
写入了各位contributors的名字, /home/Always等等,可否更新管理一下,方便大家的使用。
谢谢!
支持你们。
我使用tensorrt后端尝试使用demo,但是graph->init() 速度耗时非常久,麻烦帮忙看一下0.0 感谢!
环境:GTX 3060 cuda 11.5 Tensorrt 8.6.1.6 opencv 4.6
运行的命令:
./demo_nndeploy_detect --name NNDEPLOY_YOLOV5 --inference_type kInferenceTypeTensorRt --device_type kDeviceTypeCodeCuda:0 --model_type kModelTypeOnnx --is_path --model_value ./yolov5s.onnx --codec_flag kCodecFlagImage --parallel_type kParallelTypeSequential --input_path ./test_data_detect_sample.jpg --output_path ./sample_output.jpg
请问下如果是多个线程同时访问单个pipeline,是否线程安全?如何避免线程冲突
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