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‎.dvc/.gitignore

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/config.local
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/tmp
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/cache

‎.dvc/config

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‎.dvcignore

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# Add patterns of files dvc should ignore, which could improve
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# the performance. Learn more at
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# https://dvc.org/doc/user-guide/dvcignore

‎.github/workflows/.gitkeep

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‎.github/workflows/main.yml

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name: CML
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on: [push]
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jobs:
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train-and-report:
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runs-on: ubuntu-latest
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container: docker://ghcr.io/iterative/cml:0-dvc2-base1
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steps:
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- uses: actions/checkout@v3
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- run: |
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pip install -r requirements.txt
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python main.py # create score.json
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# Create CML report
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cat score.json >> report.md
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# echo '![](./plot.png "Confusion Matrix")' >> report.md
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env:
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REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}

‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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ml-environment/
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artifacts/
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data/
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LOGS/
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# ml-environment
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# Spyder project settings
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.spyderproject
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.spyproject
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artifacts
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/

‎LICENSE

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MIT License
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Copyright (c) 2023 Aman Kumar
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# End-to-End-Image-Classification-MLOPS-implementation
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<img src="project-image.png">
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‎config/config.yaml

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artifacts_root: artifacts
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data_ingestion:
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root_dir: data
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source_URL: https://github.com/Aman123lug/Data-storage-for-all-projects/raw/main/Chicken-fecal-images.zip
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local_data_file: artifacts/data_ingestion/data.zip
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unzip_dir: artifacts/data_ingestion
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prepare_base_model:
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root_dir: artifacts/prepare_base_model
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base_model_config: artifacts/prepare_base_model/base_model.h5
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updated_base_model_config: artifacts/prepare_base_model/base_model_updated.h5
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prepare_callbacks:
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root_dir: artifacts/prepare_callbacks
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tensorboard_root_log_dir: artifacts/prepare_callbacks/tensorboard_root_log_dir
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checkpoint_model_filepath: artifacts/prepare_callbacks/checkpoint_root_log_dir/model.h5
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training:
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root_dir: artifacts/training
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train_model_path: artifacts/training/model.h5

‎dvc.yaml

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stages:
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data_ingestion:
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cmd: python src/cnnClassifier/pipline/stage_01_data_ingestion.py
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deps:
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- src/cnnClassifier/pipline/stage_01_data_ingestion.py
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- config/config.yaml
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outs:
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- data/Chicken-fecal-images
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prepare_base_model:
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cmd: python src/cnnClassifier/pipline/stage_02_prepare_base_model.py
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deps:
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- src/cnnClassifier/pipline/stage_02_prepare_base_model.py
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- config/config.yaml
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params:
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- IMAGE_SIZE
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- INCLUDE_TOP
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- CLASSES
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- WEIGHTS
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- LEARNING_RATE
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outs:
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- artifacts/prepare_base_model
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training:
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cmd: python src/cnnClassifier/pipline/stage_03_training.py
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deps:
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- src/cnnClassifier/pipline/stage_03_training.py
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- src/cnnClassifier/components/prepare_callbacks.py
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- config/config.yaml
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- data/Chicken-fecal-images
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- artifacts/prepare_base_model
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params:
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- EPOCH
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- BATCH_SIZE
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- IMAGE_SIZE
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- AUGMENTATION
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outs:
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- artifacts/training/model.h5
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model_evaluation:
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cmd: python src/cnnClassifier/pipline/stage_04_evaluation.py
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deps:
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- src/cnnClassifier/pipline/stage_04_evaluation.py
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- config/config.yaml
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- data/Chicken-fecal-images
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- artifacts/training/model.h5
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params:
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- IMAGE_SIZE
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- BATCH_SIZE
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metrics:
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- score.josn:
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cache: false

‎main.py

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from src.cnnClassifier.loggerr import logger
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from src.cnnClassifier.pipline.stage_01_data_ingestion import DataIngestionTrainingPipeline
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from src.cnnClassifier.pipline.stage_02_prepare_base_model import PrepareBaseModelTrainingPipeline
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from src.cnnClassifier.pipline.stage_03_training import ModelTrainingPipeline
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from src.cnnClassifier.pipline.stage_04_evaluation import ModelEvaluation
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STAGE_NAME = "Data Ingestion"
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try:
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logger.info(f" <<<< stage {STAGE_NAME} <<<< started")
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data_ingestion = DataIngestionTrainingPipeline()
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data_ingestion.main()
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logger.info(f" <<<< stage {STAGE_NAME} >>>> completed !")
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except Exception as e:
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# logger.exception(e)
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raise e
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STAGE_NAME = "Prepare Base Model"
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try:
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logger.info(f" <<<< stage {STAGE_NAME} <<<< started")
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base_model = PrepareBaseModelTrainingPipeline()
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base_model.main()
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logger.info(f" <<<< stage {STAGE_NAME} >>>> completed !")
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except Exception as e:
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logger.exception(e)
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raise e
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STAGE_NAME = "Training"
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try:
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logger.info(f"<<<< stage {STAGE_NAME} started >>>>")
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train = ModelTrainingPipeline()
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train.main()
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logger.info(f"<<<< stage {STAGE_NAME} completed >>>>")
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except Exception as e:
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logger.exception(e)
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raise e
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STAGE_NAME = "Model Evaluation"
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try:
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logger.info(f"<<<< Stage {STAGE_NAME} started")
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evaluate = ModelEvaluation()
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evaluate.main()
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logger.info(f"<<<< stage {STAGE_NAME} completed >>>>")
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except Exception as e:
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logger.exception(e)
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raise e

‎params.yaml

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AUGMENTATION: True
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IMAGE_SIZE: [224, 224, 3]
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BATCH_SIZE: 16
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INCLUDE_TOP: False
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EPOCH: 1
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LEARNING_RATE: 0.01
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CLASSES: 2
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WEIGHTS: imagenet
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‎project-image.png

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‎requirements.txt

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scikit-learn
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tensorflow
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dvc
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pandas
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numpy
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notebook
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matplotlib
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seaborn
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python-box==6.0.2
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pyYAML
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tqdm
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ensure==1.0.2
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joblib
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spacy
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Flask
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Flask-Cors
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-e .

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