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Vitis AI is Xilinx’s development stack for AI inference on Xilinx hardware platforms, including both edge devices and Alveo cards.
Use Vitis AI to deploy yolov5 on ZCU104
分别使用OpenCV、ONNXRuntime部署yolov5-v6.1目标检测，包含C++和Python两个版本的程序。支持yolov5s，yolov5m，yolov5l，yolov5n，yolov5x，yolov5s6，yolov5m6，yolov5l6，yolov5n6，yolov5x6的十种结构的yolov5-v6.1
LAVIS - A One-stop Library for Language-Vision Intelligence
The offical code for paper "Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection"
Official code of the paper "Few-Shot Object Detection via Variational Feature Aggregation" (AAAI 2023)
Code for reproducing the results in our TGRS-2024 paper Few-Shot Object Detection in Remote Sensing Images via Label-Consistent Classifier and Gradual Regression.
Full conference version of AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
AISystem 主要是指AI系统，包括AI芯片、AI编译器、AI推理和训练框架等AI全栈底层技术
A biblatex implementation of the GB/T7714-2015 bibliography style || GB/T 7714-2015 参考文献著录和标注的biblatex样式包
pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...
Based on the mmdetection framework, compute various salience maps for object detection.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Official implementation of the CVPR 2022 paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".
[NeurIPS 2021 Spotlight] Aligning Pretraining for Detection via Object-Level Contrastive Learning
RSVG: Exploring Data and Model for Visual Grounding on Remote Sensing Data, 2022
This is a repository for ACMMM22 paper "Exploring Effective Knowledge Transfer for Few-shot Object Detection"
Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020
The code for “Multiscale Object Contrastive Learning–Derived Few-Shot Object Detection in VHR imagery”