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assignment2

This is our source code for Cluster and Cloud Computing assignment2

cellpose

a generalist algorithm for cellular segmentation with human-in-the-loop capabilities

cloud-computing

cloud-computing1

code-library

some code for me

convnext

Code release for ConvNeXt model

declarative-programming

dinov2

PyTorch code and models for the DINOv2 self-supervised learning method.

efficientdet

EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow

faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

git-tutorial

gmflow

[CVPR 2022 Oral] GMFlow: Learning Optical Flow via Global Matching

gpu-puzzles

Solve puzzles. Learn CUDA.

hello-world

first repository at github

ios

ki67_proj

maskdino

[CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation"

maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

mobile-programming

for the subject mobile programming

paddle

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

python

python learning

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

pytorch-nested-unet

PyTorch implementation of UNet++ (Nested U-Net).

raft

fork it

simpledet

A Simple and Versatile Framework for Object Detection and Instance Recognition

snapchat

ultralytics

NEW - YOLOv8 🚀 in PyTorch > ONNX > CoreML > TFLite

vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

yolov6

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

yolov9

Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information