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Opensourcing my Client approved Image Classification Pipeline

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MvMukesh/ImageClassifier-Pipeline

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Common WorkFlow to consider

  1. Write config.yaml code
  2. Write secrets.yaml code (senstive data, say if in case you want to connect to db and putting in credentials) [Optinal to cases]
  3. Write params.yaml code (general parameters values)
  4. Write entity module code

Getting inside src

  1. Write config manager present in src config
  2. Write components one after others say (data_ingestion, model_training, model_evaluation .... so on)
  3. Write pipeline code
  4. Write end point code in main.py

Tree

.
├── artifacts
│   └── data_ingestion
│       ├── classifier_data_v01
│       └── dataV01.zip
├── config
│   └── config.yaml
├── LICENSE
├── logs
│   └── running_logs.log
├── main.py
├── params.yaml
├── README.md
├── requirements.txt
├── research_env
│   ├── check_baseModel_v04.ipynb
│   ├── check_dataIngestion_v03.ipynb
│   └── trials_n.ipynb
├── setup.py
├── src
│   ├── imageClassifier
│   │   ├── components
│   │   │   ├── data_ingestion.py
│   │   │   ├── __init__.py
│   │   │   └── modelPrep_base.py
│   │   ├── config
│   │   │   ├── configuration.py
│   │   │   └── __init__.py
│   │   ├── constants
│   │   │   └── __init__.py
│   │   ├── entity
│   │   │   ├── config_entity.py
│   │   │   └── __init__.py
│   │   ├── __init__.py
│   │   ├── pipeline
│   │   │   ├── data_ingestion_v01.py
│   │   │   ├── __init__.py
│   │   │   └── modelPrep_base_v02.py
│   │   └── utils
│   │       ├── common.py
│   │       └── __init__.py
│   └── imageClassifier.egg-info
│       ├── dependency_links.txt
│       ├── PKG-INFO
│       ├── SOURCES.txt
│       └── top_level.txt
├── template.py
└── test.py

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