EverestRs's repositories
YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
MIT6.5940
模型加速/模型压缩
PoET-Pose-Estimation-Transformer-
PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation
Awesome-System-for-Machine-Learning
A curated list of research in machine learning systems (MLSys). Paper notes are also provided.
once-for-all
[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
contextual-pruning
Library to facilitate pruning of LLMs based on context
smoothquant
[ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
MIT6.5940-EfficientML
MIT6.5940-EfficientML lab in 2023fall
MIT-6.5940
All Homeworks for TinyML and Efficient Deep Learning Computing 6.5940 • Fall • 2023 • https://efficientml.ai
LLaMA2-7B-on-laptop
Lab 5 project of MIT-6.5940, deploying LLaMA2-7B-chat on one's laptop with TinyChatEngine.
mmrazor
OpenMMLab Model Compression Toolbox and Benchmark.
EdgeML
This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
Efficient-AI
Learn TinyML and Efficient Deep Learning Computing 6.5940 Fall 2023
object-detect
本仓库存放的是目标检测YOLO系列的一些代码以及改进模块的代码实现，需要的小伙伴自取就可以啦~
yolact
A simple, fully convolutional model for real-time instance segmentation.
OccNet-Course
国内首个占据栅格网络全栈课程《从BEV到Occupancy Network，算法原理与工程实践》，包含端侧部署。Surrounding Semantic Occupancy Perception Course for Autonomous Driving (docs, ppt and source code)
Spresense-LowPower-EdgeAI
Sample projects for "Get started Low Power Edge AI with Spresense"
litepose
[CVPR'22] Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
tinyengine
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory
mcunet
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
MIT6.S081
操作系统