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        "description": "[![Codacy Badge](https://api.codacy.com/project/badge/Grade/20ce98b051b94e048fdb47452aa334c5)](https://app.codacy.com/app/TensorImage/tensorimage?utm_source=github.com&utm_medium=referral&utm_content=TensorImage/tensorimage&utm_campaign=Badge_Grade_Dashboard)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n[![GitHub release](https://img.shields.io/github/release/tensorimage/tensorimage.svg)](https://GitHub.com/tensorimage/tensorimage/releases/)\n\n# TensorImage\n![Example](https://cdn-images-1.medium.com/max/1600/1*PAqzvCxPjpDN8RC9HQw45w.jpeg) \n\n\n__TensorImage__ is an open source library designed to make training and deploying image classification models easy.\n\n## Features\n- Cluster training: automatically compare the performance of multiple trainers, speeding up the process of hyperparameter tuning and feature engineering, as there is no need to do it manually\n\n- Multithreaded training: by default, all training operations are run in 10 threads to make training models faster\n\n- Built-in image data augmentation operations, which can be used for feature engineering:\n    - Image flipping\n    - Salt-pepper noise\n    - Random brightness\n    - Random contrast\n    - Random hue\n    - Random saturation\n    - Gaussian blur\n    - Colour filtering\n\n- Workspace organization: all datasets, trained models, and internal metadata files are stored automatically inside a workspace directory, where you can quickly find any files you need\n\n- Large-scale image classification: deploy trained models on thousands of images, with predictions for all images being stored in your workspace directory\n\n## Upcoming features\n- More data augmentation operations to apply on images:\n    - Affine/perspective transformations\n    - Random zooming\n    - Random cropping\n    - Individual pepper and salt noise\n    - More image blurring techniques:\n        - Median blur\n        - Average blur\n        - Motion blur\n        - Bilateral blur\n    - Translation\n\n\n- Option to apply different data augmentation operations at once, e.g: instead of only applying gaussian blur, to be able to apply gaussian blur, pepper salt noise and random contrast at once, not uniquely separately\n\n- Model inference for individual/batches of images for real-time prediction without writing on disk\n\n- Real-time training from individual/batches of images without reading from disk, automatically training the model from new data, linked to real-time inference without having to store the model in disk (with option to store available) \n\n## Installation\nFrom the terminal:\n```shell\n$ pip3 install tensorimage\n```\n\n## Documentation\nYou can view TensorImage's documentation [here](https://tensorimage.readthedocs.io/en/latest/).\n\n## Support\nIf you are experiencing any errors or bugs, please report them in the [issues section](https://github.com/TensorImage/TensorImage/issues) or contact us at tensor.image2@gmail.com\n\n## Contributing\nIf you have any ideas for features that should be added to TensorImage, please feel free to [fork](https://github.com/TensorImage/tensorimage/network/members) TensorImage and [open a pull request](https://github.com/TensorImage/tensorimage/pulls).\n\n## License\nTensorImage is licensed under the [MIT](https://github.com/TensorImage/tensorimage/blob/master/LICENSE.md) license.\n\n\n",
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