# FeiYull/TensorRT-Alpha

🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎

Repository: https://github.com/FeiYull/TensorRT-Alpha
Canonical: https://ross.abutalabs.com/products/tensorrt-alpha
Language: C++
License: GPL-2.0
License Family: copyleft
Topics: yolov8, yolov7, cuda, libfacedetection, tensorrt, u2net, yolor, yolov3, yolov4, yolov5, yolov6, yolox, efficientdet, rt-detr, yolonas, yolov8-pose, yolov8-seg
Last push: 2024-07-19T13:17:20+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1455, "days_push": 775, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1460, forks 200 (observed 2026-08-28T04:04:47.329946+00:00)

## What it is
A C++/CUDA library providing TensorRT-accelerated deployment for 30+ popular computer vision models including YOLOv3-v8, YOLOv8-Pose/Seg/Cls, YOLOX, YOLO NAS, EfficientDet, RT-DETR, U2Net, and libfacedetection. It implements multi-batch CUDA preprocessing, inference, decoding, and NMS on GPU, with model conversion via torch->onnx->tensorrt.

## Use cases
- deploy yolov8 with tensorrt in c++
- accelerate yolo inference on nvidia gpu
- run object detection models fast with cuda
- convert pytorch models to tensorrt engines
- deploy yolov8-pose and yolov8-seg on gpu
- batch preprocess and run nms on cuda
- deploy detection models on ubuntu or windows with tensorrt

## When to choose
- you need maximum GPU inference speed for YOLO-family or other CV models in C++
- you deploy on NVIDIA hardware with TensorRT and want CUDA preprocessing/decoding/NMS included
- you need pose estimation, segmentation, or detection variants of YOLOv8 in one deployment library

## When to avoid
- you want a simple Python-only inference pipeline
- you have no NVIDIA GPU or cannot install CUDA/TensorRT
- you need models outside the supported CV detection/segmentation set
- you need a permissively licensed dependency (it is GPL-2.0)

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision, gpu-computing, llm-inference
- domain: computer-vision, deep-learning, machine-learning, gpu-computing, image-processing
- platform: windows, cpp
- tags: tensorrt, cuda, yolo, object-detection, model-deployment, inference-optimization, nvidia, linux, gpu

## Member repositories
- FeiYull/TensorRT-Alpha (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:47.329946+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:35:22.673247+00:00, confidence not recorded.
  - readme: https://github.com/FeiYull/TensorRT-Alpha (fetched 2026-08-28T04:04:47.329946+00:00, sha 2fb4a2aecfe3)
- Data as of 2026-08-30T08:39:29.467469+00:00.