tensorrtx
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Implementation of popular deep learning networks with TensorRT network definition API
## Env - Xavier NX. - Ubuntu20.04, - Cuda 11.4 cudnn 8.6 - TensorRT 8.2.4.2 ## About this repo - tensorrtx-yolov5-v7.0 ## Your problem when i use tensorttx yolov5s.wts to...
## Env - GPU: RTX 3080. Env - GPU: RTX 3080. - OS, Win10. - Cuda version 11.8 - TensorRT version 8.6.1 我在自己训练好的模型转wts的时候失败了,提示找不到模块 No module named 'ultralytics.utils',这个pt模型是我用yolov8s.pt进行训练得到的,报错信息如下 ![image](https://github.com/wang-xinyu/tensorrtx/assets/108455796/06fba6ea-8434-48a8-a0a1-d7c4e929ddd5) 但是,我试着转官方提供的yolov8n.pt时却能正确转换wts文件,不知道为啥会有这种问题.... 自定义数据集的best.pt模型是我正常训练yolov8s得到的,之前搞其他C++部署时,转onnx模型是可以正常使用的。...
## Env - GPU, e.g. RTX2080 - OS, e.g. Ubuntu20.04 - Cuda version - TensorRT version 8.5.1 ## About this repo - which branch/tag/commit are you using? - which model?...
- GPU, e.g. GTX1650 - OS, e.g. Ubuntu22.04 - Cuda version 12.2 - TensorRT version 8.6.1 ## About this repo .engine file is generating but when i am running inferencing...
## Env - docker nvcr.io/nvidia/tensorrt:24.01-py3 - GPU, GeForce RTX 2060 - OS, Ubuntu18.04 - Cuda 12.0 - TensorRT 8.6.1.6-1 ## About this repo repo wang-xinyu/tensorrtx/retinaface model retinaface Hello, The FP16...
## Env - GPU : Jetson Orin AGX 64 Gb dev liot - OS : Ubuntu 20.04 - Cuda version : 11.4 - TensorRT version : 8,5,2,1 ## About this...
## Env - GPU, e.g. V100, RTX2080, TX2, Xavier NX, Nano, etc. - OS, e.g. Ubuntu16.04, Win10, etc. - Cuda version - TensorRT version ## About this repo - which...
## Env - nvidia orin xavier, jetpack 5.0.2 ## Your problem 我用yolov8n.pt可以顺利的生成.engine,然后用自己训练的best.pt转换一直报错,已经修改config.h和model.cpp,麻烦大佬看一下怎么修改,多谢 ![Screenshot from 2024-02-27 15-46-09](https://github.com/wang-xinyu/tensorrtx/assets/45551492/1cd1a35b-bcb8-4852-8404-5e7740f7dc91) ![Screenshot from 2024-02-27 15-57-21](https://github.com/wang-xinyu/tensorrtx/assets/45551492/150f24ef-74a5-47e9-9ea0-f26af5a64391) ![Screenshot from 2024-02-27 15-57-29](https://github.com/wang-xinyu/tensorrtx/assets/45551492/4d8ae1f1-68d4-4417-a39f-87aff69105b0)