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
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations)

Graph Neural Networks with Learnable Structural and Positional Representations Source code for the paper "Graph Neural Networks with Learnable Structu

Vijay Prakash Dwivedi 180 Dec 22, 2022
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking Updates 08/2021: check out our domain adaptation for video segmentation paper Domain A

17 Nov 30, 2022
GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification

GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification This is the official pytorch implementation of t

Alibaba Cloud 5 Nov 14, 2022
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

Felix Wimbauer 494 Jan 06, 2023
Official repository for Fourier model that can generate periodic signals

Conditional Generation of Periodic Signals with Fourier-Based Decoder Jiyoung Lee, Wonjae Kim, Daehoon Gwak, Edward Choi This repository provides offi

8 May 25, 2022
Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly

Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly Code for this paper Ultra-Data-Efficient GAN Tra

VITA 77 Oct 05, 2022
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation (ICCV 2021)

Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation Home | PyTorch BigGAN Discovery | TensorFlow ProGAN Regulariza

Yuxiang Wei 54 Dec 30, 2022
An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise

45 Dec 08, 2022
Original Pytorch Implementation of FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation

FLAME Original Pytorch Implementation of FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation, accepted at the 17th IEEE Internation Co

Neelabh Sinha 19 Dec 17, 2022
A real world application of a Recurrent Neural Network on a binary classification of time series data

What is this This is a real world application of a Recurrent Neural Network on a binary classification of time series data. This project includes data

Josep Maria Salvia Hornos 2 Jan 30, 2022
Object Tracking and Detection Using OpenCV

Object tracking is one such application of computer vision where an object is detected in a video, otherwise interpreted as a set of frames, and the object’s trajectory is estimated. For instance, yo

Happy N. Monday 4 Aug 21, 2022
This is an official pytorch implementation of Fast Fourier Convolution.

Fast Fourier Convolution (FFC) for Image Classification This is the official code of Fast Fourier Convolution for image classification on ImageNet. Ma

pkumi 199 Jan 03, 2023
Stitch it in Time: GAN-Based Facial Editing of Real Videos

STIT - Stitch it in Time [Project Page] Stitch it in Time: GAN-Based Facial Edit

1.1k Jan 04, 2023
PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

PointCNN: Convolution On X-Transformed Points Created by Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. Introduction PointCNN

Yangyan Li 1.3k Dec 21, 2022
Scalable machine learning based time series forecasting

mlforecast Scalable machine learning based time series forecasting. Install PyPI pip install mlforecast Optional dependencies If you want more functio

Nixtla 145 Dec 24, 2022
This code is an implementation for Singing TTS.

MLP Singer This code is an implementation for Singing TTS. The algorithm is based on the following papers: Tae, J., Kim, H., & Lee, Y. (2021). MLP Sin

Heejo You 22 Dec 23, 2022
Deep Multimodal Neural Architecture Search

MMNas: Deep Multimodal Neural Architecture Search This repository corresponds to the PyTorch implementation of the MMnas for visual question answering

Vision and Language Group@ MIL 23 Dec 21, 2022
A hifiasm fork for metagenome assembly using Hifi reads.

hifiasm_meta - de novo metagenome assembler, based on hifiasm, a haplotype-resolved de novo assembler for PacBio Hifi reads.

44 Jul 10, 2022
Yolox-bytetrack-sample - Python sample of MOT (Multiple Object Tracking) using YOLOX and ByteTrack

yolox-bytetrack-sample YOLOXとByteTrackを用いたMOT(Multiple Object Tracking)のPythonサン

KazuhitoTakahashi 12 Nov 09, 2022