Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
    Links to: arxiv.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: YogevKr
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 839 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed about 3 years ago
Metadata Files
Readme Citation

README.md

Learning to Score

Docker Image CI

This repository is the official implementation of TBD <!-- My Paper Title. --> <!--

Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials -->

Requirements

To install requirements:

setup pip install -r requirements.txt <!--

Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc... -->

Training

To train the model(s) in the paper, run this command:

train python train.py --input-data <path_to_data> --alpha 10 --beta 20 <!--

Describe how to train the models, with example commands on how to train the models in your paper, including the full training procedure and appropriate hyperparameters. -->

Evaluation

To evaluate my model on ImageNet, run:

eval python eval.py --model-file mymodel.pth --benchmark imagenet <!--

Describe how to evaluate the trained models on benchmarks reported in the paper, give commands that produce the results (section below). --> <!--

Pre-trained Models

You can download pretrained models here:

Give a link to where/how the pretrained models can be downloaded and how they were trained (if applicable). Alternatively you can have an additional column in your results table with a link to the models. -->

Owner

  • Name: Yogev Kriger
  • Login: YogevKr
  • Kind: user
  • Location: Israel
  • Company: New Relic

GitHub Events

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Dependencies

requirements-dev.txt pypi
  • jupyterlab * development
  • torchsummary * development
  • umap-learn * development
  • wandb * development
requirements.txt pypi
  • networkx *
  • numpy *
  • pytorch_lightning *
  • torch *
  • torchmetrics *
  • wandb *
.github/workflows/docker-image.yml actions
  • docker/build-push-action v2 composite
  • docker/login-action v1 composite
  • docker/setup-buildx-action v1 composite
  • docker/setup-qemu-action v1 composite
Dockerfile docker
  • pytorch/pytorch latest build