All Projects → talebolano → TensorRT-solo-python

talebolano / TensorRT-solo-python

Licence: other
TensorRT for SOLO(use python)

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to TensorRT-solo-python

flexinfer
A flexible Python front-end inference SDK based on TensorRT
Stars: ✭ 83 (+260.87%)
Mutual labels:  tensorrt
yolov5 tensorrt int8 tools
tensorrt int8 量化yolov5 onnx模型
Stars: ✭ 105 (+356.52%)
Mutual labels:  tensorrt
ROS-Object-Detection-2Dto3D-RealsenseD435
Use the Intel D435 real-sensing camera to realize object detection based on the Yolov3-5 framework under the Opencv DNN(old version)/TersorRT(now) by ROS-melodic.Real-time display of the Pointcloud in the camera coordinate system.
Stars: ✭ 45 (+95.65%)
Mutual labels:  tensorrt
tensorflow-tensorrt
Tensorflow to TensorRT Model Converter
Stars: ✭ 30 (+30.43%)
Mutual labels:  tensorrt
Scaled-YOLOv4-TensorRT
Got 100fps on TX2. Got 500fps on GeForce GTX 1660 Ti. If the project is useful to you, please Star it.
Stars: ✭ 169 (+634.78%)
Mutual labels:  tensorrt
FAST-Pathology
⚡ Open-source software for deep learning-based digital pathology
Stars: ✭ 54 (+134.78%)
Mutual labels:  tensorrt
MutualGuide
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection
Stars: ✭ 97 (+321.74%)
Mutual labels:  tensorrt
ONNX-Runtime-with-TensorRT-and-OpenVINO
Docker scripts for building ONNX Runtime with TensorRT and OpenVINO in manylinux environment
Stars: ✭ 15 (-34.78%)
Mutual labels:  tensorrt
yolov5-deepsort-tensorrt
A c++ implementation of yolov5 and deepsort
Stars: ✭ 207 (+800%)
Mutual labels:  tensorrt
YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
Stars: ✭ 6,570 (+28465.22%)
Mutual labels:  tensorrt
libdeepvac
Use PyTorch model in C++ project
Stars: ✭ 98 (+326.09%)
Mutual labels:  tensorrt
YOLOv5-Lite
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
Stars: ✭ 1,230 (+5247.83%)
Mutual labels:  tensorrt
vs-mlrt
Efficient ML Filter Runtimes for VapourSynth (with built-in support for waifu2x, DPIR, RealESRGANv2, and Real-CUGAN)
Stars: ✭ 34 (+47.83%)
Mutual labels:  tensorrt
keras-tensorrt-jetson
Example of loading a Keras model into TensorRT C++ API
Stars: ✭ 51 (+121.74%)
Mutual labels:  tensorrt
InferenceHelper
C++ Helper Class for Deep Learning Inference Frameworks: TensorFlow Lite, TensorRT, OpenCV, OpenVINO, ncnn, MNN, SNPE, Arm NN, NNabla, ONNX Runtime, LibTorch, TensorFlow
Stars: ✭ 142 (+517.39%)
Mutual labels:  tensorrt
tensorrt-zoo
openpose, yolov3 with tiny-tensorrt
Stars: ✭ 84 (+265.22%)
Mutual labels:  tensorrt
yolov4-triton-tensorrt
This repository deploys YOLOv4 as an optimized TensorRT engine to Triton Inference Server
Stars: ✭ 224 (+873.91%)
Mutual labels:  tensorrt
person-detection
TensorRT person tracking RFBNet300
Stars: ✭ 30 (+30.43%)
Mutual labels:  tensorrt
Yolov3-TensorRT-py
Yolov3 on tensorflow2.0 and tensorrt7.0
Stars: ✭ 15 (-34.78%)
Mutual labels:  tensorrt
play with tensorrt
Sample projects for TensorRT in C++
Stars: ✭ 39 (+69.57%)
Mutual labels:  tensorrt

TensorRT SOLO (Python)

996.icu

TensorRT for SOLO(use python)

Enviroments

TensorRT >=7.1
Ubuntu 18.04

A quick demo

1. Convert solo model form pytorch to onnx

python3 get_onnx.py --config ${SOLO_path}/configs/solov2_r101_fpn_8gpu_3x.py --checkpoint ${SOLO_path}/work_dirs/SOLOv2_R101_3x.pth --outputname solov2_r101.onnx 

2.Genrate tensorRT engine and inference

python3 inference.py --onnx_path solov2_r101.onnx --engine_path solov2_101.engine --mode fp16 --image_path ${your_picture_path} --save --show

Inference performance(only inference time in GPU)

GPU Model Mode Inference time
V100 solov2 r101 fp16 35ms
Xavier solov2 r101 fp16 150ms
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].