rocks-classifier 0.1.1

Rock classifier deployed on railway and monitored using Weights and Biases!

Whats-this-rock

This project deploys a telegram bot that classifies rock images into 1 of 7 types.

This package uses tensorflow to accelerate deep learning experimentation.

MLOps workflow like

• Experiment Tracking
• Model Management
• Hyperparameter Tuning

was all done using Weights & Biases

Additionally, nbdev was used to

• develop the package
• produce documentation based on a series of notebooks.
• CI
• publishing to PyPi

Inspiration

The common complaint that you need massive amounts of data to do deep learning  can be a very long way from the truth!

You very often don’t need much data at all, a lot of people are looking for ways to share data and aggregate data, but that’s unnecessary.They assume they need more data than they do, cause they’re not familiar with the basics of transfer learning which is this critical technique for needing orders of magnitudes less data.

Jeremy Howards

Installation & Training Steps

Install

To install, use pip:

pip install git+https://github.com/udaylunawat/Whats-this-rock.git

Use the Telegram Bot

You can try the bot here on Telegram.

Type /help to get instructions in chat.

Deploy Telegram Bot

rocks_deploy_bot

Download and process data

%%bash
rocks_process_data  --config-dir configs \
                    remove_bad= True \
                    remove_misclassified= True \
                    remove_duplicates= True \
                    remove_corrupted= True \
                    remove_unsupported= True \
                    sampling=None \
                    train_split=0.8 \

Train Model

Run these commands

rocks_train_model   --config-dir configs \
                    wandb.project=Whats-this-rock \
                    wandb.mode=offline \
                    wandb.use=False \
                    dataset_id=[1,2] \
                    epochs=30 \
                    lr=0.005 \
                    augmentation=None \
                    monitor=val_loss \
                    loss=categorical_crossentropy \
                    backbone=resnet \
                    lr_schedule=cosine_decay_restarts \
                    lr_decay_steps=300 \
                    trainable=False \

You can try different models and parameters by editing configs/config.yaml.

By using Hydra it’s now much more easier to override parameters like this

rocks_train_model wandb.project=Whats-this-rock \
                  dataset_id=[1,2] \
                  epochs=50 \
                  backbone=resnet

Wandb Sweeps (Hyperparameter Tuning)

Edit configs/sweeps.yaml

wandb sweep \
--project Whats-this-rock \
--entity udaylunawat \
configs/sweep.yaml

This will return a command with $sweepid

wandb agent udaylunawat/Whats-this-rock/$sweepid

Demo

Run in Colab	View Source on GitHub	Download Notebook

Features

\Features added

\Features planned

Wandb Datasets 4 Datasets Augmentation keras-cv Regular Augmentation Sampling Oversampling Undersampling Class weights Remove Corrupted Images Try Multiple Optimizers (Adam, RMSProp, AdamW, SGD) Generators TFDS datasets ImageDataGenerator Models ConvNextTiny BaselineCNN Efficientnet Resnet101 MobileNetv1 MobileNetv2 Xception LRScheduleer, LRDecay Baseline without scheduler Step decay Cosine annealing Classic cosine annealing with bathc steps w/o restart Model Checkpoint, Resume Training Evaluation Confusion Matrix Classification Report Deploy Telegram Bot Heroku - Deprecated Railway Show CM and CL in bot Docker GitHub Actions Deploy Bot when bot.py is updated. Lint code using GitHub super-linter Configuration Management ml-collections Hydra Performance improvement Convert to tf.data.Dataset Linting & Formatting Black Flake8 isort pydocstyle Add Badges Linting found the classes that the model is performing terribly on nbdev CI documentation

Deploy to Huggingface spaces Accessing the model through FastAPI (Backend) Streamlit (Frontend) convert models.py to Classes and more OOP style Group Runs kfold cross validation WandB Tables find the long tail examples or hard examples, Add Badges Railway

Technologies Used

Directory Tree

├── imgs                              <- Images for skill banner, project banner and other images
│
├── configs                           <- Configuration files
│   ├── configs.yaml                  <- config for single run
│   └── sweeps.yaml                   <- confguration file for sweeps hyperparameter tuning
│
├── data
│   ├── corrupted_images              <- corrupted images will be moved to this directory
│   ├── misclassified_images          <- misclassified images will be moved to this directory
│   ├── bad_images                    <- Bad images will be moved to this directory
│   ├── duplicate_images              <- Duplicate images will be moved to this directory
│   ├── sample_images                 <- Sample images for inference
│   ├── 0_raw                         <- The original, immutable data dump.
│   ├── 1_external                    <- Data from third party sources.
│   ├── 2_interim                     <- Intermediate data that has been transformed.
│   └── 3_processed                   <- The final, canonical data sets for modeling.
│
├── notebooks                         <- Jupyter notebooks. Naming convention is a number (for ordering),
│                                        the creator's initials, and a short `-` delimited description, e.g.
│                                        1.0-jqp-initial-data-exploration`.
│
│
├── rocks_classifier                  <- Source code for use in this project.
│   │
│   ├── data                          <- Scripts to download or generate data
│   │   ├── download.py
│   │   ├── preprocess.py
│   │   └── utils.py
│   │
│   ├── callbacks                     <- functions that are executed during training at given stages of the training procedure
│   │   └── callbacks.py
│   │
│   ├── models                        <- Scripts to train models and then use trained models to make
│   │   │                                predictions
│   │   ├── evaluate.py
│   │   ├── models.py
│   │   ├── predict.py
│   │   ├── train.py
│   │   └── utils.py
│   │
│   │
│   └── visualization                 <- Scripts for visualizations
│
├── .dockerignore                     <- Docker ignore
├── .gitignore                        <- GitHub's excellent Python .gitignore customized for this project
├── LICENSE                           <- Your project's license.
├── README.md                         <- The top-level README for developers using this project.
├── CHANGELOG.md                      <- Release changes.
├── CODE_OF_CONDUCT.md                <- Code of conduct.
├── CONTRIBUTING.md                   <- Contributing Guidelines.
├── settings.ini                      <- configuration.
├── README.md                         <- The top-level README for developers using this project.
├── requirements.txt                  <- The requirements file for reproducing the analysis environment, e.g.
│                                        generated with `pip freeze > requirements.txt`
└── setup.py                          <- makes project pip installable (pip install -e .) so src can be imported

Bug / Feature Request

If you find a bug (the site couldn’t handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result.

If you’d like to request a new function, feel free to do so by opening an issue here. Please include sample queries and their corresponding results.

Contributing

• Contributions make the open source community such an amazing place to learn, inspire, and create.
• Any contributions you make are greatly appreciated.
• Check out our contribution guidelines for more information.

License

LinkFree is licensed under the MIT License - see the LICENSE file for details.

Credits

• Dataset 1 - by Mahmoud Alforawi
• Dataset 2 - by salmaneunus
• nbdev inspiration - tmabraham

Support

This project needs a ⭐️ from you. Don’t forget to leave a star ⭐️

Walt might be the one who knocks
but Hank is the one who rocks.