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align=\"center\"\u003e\n  \u003cimg src=\"images/HeroPolaroids.png\" alt=\"Hero\" width=\"700\"\u003e\n\u003c/p\u003e\n\n[![Vercel](https://img.shields.io/badge/Live%20Demo-Vercel-000000?logo=vercel\u0026logoColor=white)](https://midnight-train-explorer.vercel.app/)\n[![React](https://img.shields.io/badge/Built%20with-React-61DAFB?logo=react\u0026logoColor=white)](https://react.dev/)\n[![Next.js](https://img.shields.io/badge/Built%20with-Next.js-000000?logo=nextdotjs\u0026logoColor=white)](https://nextjs.org/)\n[![Tailwind CSS](https://img.shields.io/badge/Styled%20with-Tailwind%20CSS-38B2AC?logo=tailwindcss\u0026logoColor=white)](https://tailwindcss.com/)\n[![D3](https://img.shields.io/badge/Built%20with-D3-F9A03C?logo=d3\u0026logoColor=white)](https://d3js.org/)\n[![Weaviate](https://img.shields.io/badge/Powered%20by-Weaviate-orange?logo=github)](https://github.com/weaviate/weaviate)\n\n![Crystallization](https://img.shields.io/badge/domain-Crystallization-white)\n[![](https://img.shields.io/badge/Kaggle-Dataset-green?logo=kaggle\u0026logoColor=white)](https://www.kaggle.com/datasets/opencrystaldata/cephalexin-reactive-crystallization)\n![Binary Classification](https://img.shields.io/badge/task-Binary_Classification-yellowgreen)\n![ResNet](https://img.shields.io/badge/model-ResNet-yellow)\n![PyCharm](https://img.shields.io/badge/PyCharm-2023.2.4-lightorange)\n![MIT License](https://img.shields.io/badge/License-MIT-green)\n\n\n## Really quick start. \nSee the live demo now by clicking [here.](https://midnight-train-explorer.vercel.app/)  \n\n## Quick start. \nClone the repository by downloading the code or using this at a prompt: \n```\n git clone https://github.com/KatherineMossDeveloper/Midnight-Train.git\n```\nOpen the project in Pycharm, or your preferred editor, and start the local development server:\n```\n npm run dev\n```\nOpen the app in your browser at...\n```\n http://localhost:3000 \n```\n\n## Slow start.  \nThis project was inspired by a research paper:  Salami, H., McDonald, M. A., Bommarius, A. S., Rousseau, R. W., \u0026 Grover, M. A. (2021). [In Situ Imaging Combined with Deep Learning for Crystallization Process Monitoring: Application to Cephalexin Production](https://doi.org/10.1021/acs.oprd.1c00136). *Organic Process Research \u0026 Development*, 25, 1670–1679. \n\nThe scientists who wrote the paper trained ResNet models with ImageNet weights on the OpenCrystalData dataset. The models were trained to do binary classification of images of crystals, designating them as either CEX (a.k.a., “cephalexin antibiotic,” a good thing) or PG (a.k.a. “phenylglycine,” a bad thing).  \n\nThe Georgia Project, in this same GitHub site, recreates their work, then it stores details in a database.  \n\nMidnight train, in turn, pulls these details from the database and creates graphs in order to study the dataset.  Here is Midnight Train's detailed documentation.  \n[Go to the main doc file](docs/maindoc.md)    \n\n## Contributions.  \nIf you found an issue or would like to make a suggestion for an improvement to the code or documentation, please click on the issue tab on the project page and leave me a note.  If you like this project, leave a star.  \n\n## Known issues.  \nNone.  \n\n## Contact info.                                                                     \nFor more details about this project, feel free to reach out to me at katherinemossdeveloper@gmail.com or my account on [LinkedIn](https://www.linkedin.com/pub/katherine-moss/3/b49/228).  My time zone is EST in the U.S.\n\n[back to top](#content) \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkatherinemossdeveloper%2Fmidnight-train","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkatherinemossdeveloper%2Fmidnight-train","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkatherinemossdeveloper%2Fmidnight-train/lists"}