7 Repositories
Python int8 Libraries
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
This project aims to explore the deployment of Swin-Transformer based on TensorRT, including the test results of FP16 and INT8.
Swin Transformer This project aims to explore the deployment of SwinTransformer based on TensorRT, including the test results of FP16 and INT8. Introd
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
YOLOv5-Lite:lighter, faster and easier to deploy Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, a
shufflev2-yolov5:lighter, faster and easier to deploy
shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
EfficientNetv2 TensorRT int8
EfficientNetv2_TensorRT_int8 EfficientNetv2模型实现来自https://github.com/d-li14/efficientnetv2.pytorch 环境配置 ubuntu:18.04 cuda:11.0 cudnn:8.0 tensorrt:7
An ultra fast tiny model for lane detection, using onnx_parser, TensorRTAPI, torch2trt to accelerate. our model support for int8, dynamic input and profiling. (Nvidia-Alibaba-TensoRT-hackathon2021)
Ultra_Fast_Lane_Detection_TensorRT An ultra fast tiny model for lane detection, using onnx_parser, TensorRTAPI to accelerate. our model support for in
tensorrt int8 量化yolov5 4.0 onnx模型
onnx模型转换为 int8 tensorrt引擎