Popular GitHub Repositories for Es7

Filament是一个实时物理渲染引擎，支持Android、iOS、Windows、Linux、macOS和WebGL2平台。

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

:man: Code for "Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression"

Cool experiments at the intersection of Computer Vision and Sports ??

Streamer-Sales 销冠 —— 卖货主播 LLM 大模型??，一个能够根据给定的商品特点从激发用户购买意愿角度出发进行商品解说的卖货主播大模型。??内含详细的数据生成流程? ?另外还集成了 LMDeploy 加速推理?、RAG检索增强生成 ?、TTS文字转语音?、数字人生成 ?、 Agent 使用网络查询实时信息?、ASR 语音转文字??、Vue 生态搭建前端?、FastAPI 搭建后端??、Docker-compose 打包部署?

OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.

???? (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! ???

The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.

??? 专注于YOLO11，YOLOv8、TYOLOv12、YOLOv10、RT-DETR、YOLOv7、YOLOv5改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules?

YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)

? Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms?

:cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI

PaddleSlim is an open-source library for deep model compression and architecture search.

Fully-featured web interface for Ollama LLMs

Evolutionary Algorithm using Python, 莫烦Python 中文AI教学

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Chat with your documents using local AI

MetaSeg: Packaged version of the Segment Anything repository

This repository implements a demo of the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper.