Efficient vision foundation models for high-resolution generation and perception.

YOLO-World + EfficientViT SAM

EfficientViT is a new family of vision models for efficient high-resolution vision.

This is the model zoo for our CVPR 2023 paper: EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention

This is a warehouse for EfficientViT-pytorch-model, can be used to train your dataset

EfficientViT DC-AE Simplified

EfficientViTSAM inference using PyTorch

无人机目标检测系统源码和数据集：改进yolo11-efficientViT

Unofficial c++ LibTorch implementation of EfficientViT

Repository for AI6103 project.

EfficientViTSAM inference using ONNXRuntime

Segmentation Research

a modified YOLOv5 algorithm

基于 EfficientViT 的 Segment Anything Model，实现了轻量级、高效的图像分割功能

Potato disease classification

No description available

Adapting EfficientViT models for use with MMSegmentation

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Pretraining the EfficientViT-B4 model on the ImageNet-1k dataset

This is a warehouse for EfficientViT-SAM-pytorch-model, can be used to train your datasets.

Official repo for "Instance Segmentation in Autonomous Log Grasping Using EfficientViT-SAM MP-Former" paper

C++/OpenCV module for multimodal LiDAR-camera fusion, segmenting point clouds semantically based on camera labels using EfficientViT-SAM.

We propose the Frequency Enhancement EfficientViT Network (FENet), designed to enhance detection robustness in snowy scenarios and boost accuracy for small traffic sign.

【绿化率】遥感航拍植被区域图像分割系统源码和数据集：改进yolo11-efficientViT

This project trains a Mask R-CNN with an EfficientViT-FPN backbone to segment brain MRI FLAIR abnormalities from the TCGA-LGG dataset. It includes data augmentation, custom anchors, warmup+exponential scheduling, and supports ONNX export for deployment.

邮票图像分割系统源码＆数据集分享 [yolov8-seg-efficientViT等50+全套改进创新点发刊_一键训练教程_Web前端展示]

户外家具图像分割系统源码＆数据集分享 [yolov8-seg-efficientViT等50+全套改进创新点发刊_一键训练教程_Web前端展示]

End-to-end MLOps pipeline for multimodal e-commerce product classification (text + image) — ingestion, training, inference and monitoring.