Count GitHub Stars ⭐

Overview

Count GitHub Stars per Day

Track GitHub stars per day over a date range to measure the open-source popularity of different repositories.

Requirements

PyGitHub is required to access the GitHub REST API via Python. This library enables you to manage GitHub resources such as repositories, user profiles, and organizations in your Python applications.

pip install PyGithub

Usage

Update TOKEN to a valid GitHub access token in count_stars.py L15 and then run:

python count_stars.py

Result

When run on April 10th, 2022 result is:

Counting stars for last 30.0 days from 02 May 2022

ultralytics/yolov5                      1572 stars  (52.4/day)  :   6%|| 1572/25683 [00:16<04:15, 94.53it/s]
facebookresearch/detectron2             391 stars   (13.0/day)  :   2%|| 391/20723 [00:04<03:56, 85.86it/s]
deepmind/deepmind-research              165 stars   (5.5/day)   :   2%|| 165/10079 [00:01<01:50, 89.52it/s]
aws/amazon-sagemaker-examples           120 stars   (4.0/day)   :   2%|| 120/6830 [00:02<02:16, 49.17it/s]
awslabs/autogluon                       127 stars   (4.2/day)   :   3%|| 127/4436 [00:01<01:00, 71.45it/s]
microsoft/LightGBM                      122 stars   (4.1/day)   :   1%|          | 122/13730 [00:01<03:10, 71.54it/s]
openai/gpt-3                            95 stars    (3.2/day)   :   1%|          | 95/11225 [00:01<03:34, 52.00it/s]
apple/turicreate                        40 stars    (1.3/day)   :   0%|          | 40/10676 [00:00<02:24, 73.59it/s]
apple/coremltools                       41 stars    (1.4/day)   :   2%|| 41/2641 [00:00<00:46, 56.00it/s]
google/automl                           55 stars    (1.8/day)   :   1%|          | 55/4991 [00:00<01:25, 57.53it/s]
google-research/google-research         548 stars   (18.3/day)  :   2%|| 548/23087 [00:07<05:11, 72.37it/s]
google-research/vision_transformer      279 stars   (9.3/day)   :   6%|| 279/5043 [00:02<00:49, 95.93it/s]
google-research/bert                    283 stars   (9.4/day)   :   1%|          | 283/31066 [00:03<07:01, 73.11it/s]
NVlabs/stylegan3                        158 stars   (5.3/day)   :   4%|| 158/4045 [00:01<00:44, 86.41it/s]
Tencent/ncnn                            278 stars   (9.3/day)   :   2%|| 278/14440 [00:03<02:41, 87.55it/s]
Megvii-BaseDetection/YOLOX              273 stars   (9.1/day)   :   4%|| 273/6286 [00:02<01:04, 92.53it/s]
PaddlePaddle/Paddle                     239 stars   (8.0/day)   :   1%|| 239/18086 [00:02<03:33, 83.73it/s]
rwightman/pytorch-image-models          772 stars   (25.7/day)  :   4%|| 772/18169 [00:08<03:21, 86.24it/s]
streamlit/streamlit                     375 stars   (12.5/day)  :   2%|| 375/18834 [00:03<03:07, 98.67it/s]
explosion/spaCy                         234 stars   (7.8/day)   :   1%|          | 234/23249 [00:02<03:47, 101.24it/s]
PyTorchLightning/pytorch-lightning      407 stars   (13.6/day)  :   2%|| 407/18246 [00:04<03:02, 97.83it/s]
ray-project/ray                         545 stars   (18.2/day)  :   3%|| 545/20228 [00:05<03:03, 107.33it/s]
fastai/fastai                           136 stars   (4.5/day)   :   1%|          | 136/22202 [00:01<04:28, 82.22it/s]
AlexeyAB/darknet                        248 stars   (8.3/day)   :   1%|| 248/18993 [00:02<03:40, 84.84it/s]
pjreddie/darknet                        201 stars   (6.7/day)   :   1%|          | 201/22651 [00:02<05:13, 71.62it/s]
WongKinYiu/yolor                        92 stars    (3.1/day)   :   6%|| 92/1559 [00:01<00:16, 87.69it/s]
wandb/client                            66 stars    (2.2/day)   :   2%|| 66/3853 [00:00<00:46, 82.16it/s]
Deci-AI/super-gradients                 74 stars    (2.5/day)   :  19%|█▉        | 74/380 [00:00<00:03, 96.71it/s]
neuralmagic/sparseml                    105 stars   (3.5/day)   :  11%|| 105/947 [00:01<00:08, 101.97it/s]
mosaicml/composer                       247 stars   (8.2/day)   :  19%|█▉        | 247/1306 [00:02<00:10, 104.76it/s]
nebuly-ai/nebullvm                      205 stars   (6.8/day)   :  20%|█▉        | 205/1045 [00:02<00:08, 97.46it/s]
Done in 125.7s
Owner
Ultralytics
YOLOv5 🚀 and Vision AI ⭐
Ultralytics
Seeing if I can put together an interactive version of 3b1b's Manim in Streamlit

streamlit-manim Seeing if I can put together an interactive version of 3b1b's Manim in Streamlit Installation I had to install pango with sudo apt-get

Adrien Treuille 6 Aug 03, 2022
Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

Fre-GAN Vocoder Fre-GAN: Adversarial Frequency-consistent Audio Synthesis Training: python train.py --config config.json Citation: @misc{kim2021frega

Rishikesh (ऋषिकेश) 93 Dec 17, 2022
An implementation of MobileFormer

MobileFormer An implementation of MobileFormer proposed by Yinpeng Chen, Xiyang Dai et al. Including [1] Mobile-Former proposed in:

slwang9353 62 Dec 28, 2022
Single Image Random Dot Stereogram for Tensorflow

TensorFlow-SIRDS Single Image Random Dot Stereogram for Tensorflow SIRDS is a means to present 3D data in a 2D image. It allows for scientific data di

Greg Peatfield 5 Aug 10, 2022
Efficient 6-DoF Grasp Generation in Cluttered Scenes

Contact-GraspNet Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, Dieter

NVIDIA Research Projects 148 Dec 28, 2022
Sudoku solver - A sudoku solver with python

sudoku_solver A sudoku solver What is Sudoku? Sudoku (Japanese: 数独, romanized: s

Sikai Lu 0 May 22, 2022
torchbearer: A model fitting library for PyTorch

Note: We're moving to PyTorch Lightning! Read about the move here. From the end of February, torchbearer will no longer be actively maintained. We'll

631 Jan 04, 2023
A Python library for common tasks on 3D point clouds

Point Cloud Utils (pcu) - A Python library for common tasks on 3D point clouds Point Cloud Utils (pcu) is a utility library providing the following fu

Francis Williams 622 Dec 27, 2022
FedGS: A Federated Group Synchronization Framework Implemented by LEAF-MX.

FedGS: Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT Preparation For instructions on generating data, plea

Lizonghang 9 Dec 22, 2022
Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"

Pytorch Implementation of Augmenting Convolutional networks with attention-based aggregation This is the unofficial PyTorch Implementation of "Augment

DK 20 Sep 09, 2022
MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021)

MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021) Overview We release the code of the MVFNet (Multi-View Fusion Network).

2 Jan 29, 2022
Local-Global Stratified Transformer for Efficient Video Recognition

DualFormer This repo is the implementation of our manuscript entitled "Local-Global Stratified Transformer for Efficient Video Recognition". Our model

Sea AI Lab 19 Dec 07, 2022
Code and data (Incidents Dataset) for ECCV 2020 Paper "Detecting natural disasters, damage, and incidents in the wild".

Incidents Dataset See the following pages for more details: Project page: IncidentsDataset.csail.mit.edu. ECCV 2020 Paper "Detecting natural disasters

Ethan Weber 67 Dec 27, 2022
Construct a neural network frame by Numpy

本项目的CSDN博客链接:https://blog.csdn.net/weixin_41578567/article/details/111482022 1. 概览 本项目主要用于神经网络的学习,通过基于numpy的实现,了解神经网络底层前向传播、反向传播以及各类优化器的原理。 该项目目前已实现的功

24 Jan 22, 2022
Food Drinks and groceries Images Multi Lingual (FooDI-ML) dataset.

Food Drinks and groceries Images Multi Lingual (FooDI-ML) dataset.

41 Jan 04, 2023
This repo is official PyTorch implementation of MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices(CVPRW 2021).

Github Code of "MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices" Introduction This repo is official PyTorch implementatio

Choi Sang Bum 203 Jan 05, 2023
DiAne is a smart fuzzer for IoT devices

Diane Diane is a fuzzer for IoT devices. Diane works by identifying fuzzing triggers in the IoT companion apps to produce valid yet under-constrained

seclab 28 Jan 04, 2023
Official repository for "On Generating Transferable Targeted Perturbations" (ICCV 2021)

On Generating Transferable Targeted Perturbations (ICCV'21) Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli Paper:

Muzammal Naseer 46 Nov 17, 2022
WRENCH: Weak supeRvision bENCHmark

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

Jieyu Zhang 176 Dec 28, 2022
Using VideoBERT to tackle video prediction

VideoBERT This repo reproduces the results of VideoBERT (https://arxiv.org/pdf/1904.01766.pdf). Inspiration was taken from https://github.com/MDSKUL/M

75 Dec 14, 2022