Levi Pereira's repositories
yolov9-qat
Implementation of YOLOv9 QAT optimized for deployment on TensorRT platforms.
triton-server-yolo
This repository serves as an example of deploying the YOLO models on Triton Server for performance and testing purposes
deepstream-yolo-e2e
Implementation of End-to-End YOLO Models for DeepStream
deepstream-yolov9
Implementation of Nvidia DeepStream 7 with YOLOv9 Models.
deepstream-yolo-triton-server-rtsp-out
The Purpose of this repository is to create a DeepStream/Triton-Server sample application that utilizes yolov7, yolov7-qat, yolov9 models to perform inference on video files or RTSP streams.
triton-client-yolo
This repository utilizes the Triton Inference Server Client, which streamlines the complexity of model deployment.
Docker-Yolov7-Nvidia-Kit
This repository provides an out-of-the-box deployment solution for creating an end-to-end procedure to train, deploy, and use Yolov7 models on Nvidia GPUs using Triton Server and Deepstream.
Label_LPRNET
Label LPRNET is a graphical image annotation tool for ocr text annotation
nvdsinfer_yolo_efficient_nms
This repository provides a custom implementation of parsing function to the Gst-nvinferserver plugin when use YOLOv7/YOLOv9 model served by Triton Server using the Efficient NMS plugin exported by ONNX.
nvdsinfer_yolov7_efficient_nms
NvDsInferYolov7EfficientNMS for Gst-nvinferserver
ultralytics
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
yolo_deepstream
yolo model qat and deploy with deepstream&tensorrt
docker_images
This repository provides a set of Images Docker used on my Projects.
nvdsinfer_yolo
This repository provides a custom implementation of parsing function to DeepStream for YOLO models exported using Plugins YOLO_NMS_TRT and ROIAling_TRT.
TensorRT
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
yolov10
YOLOv10: Real-Time End-to-End Object Detection
yolov7
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors