Metadata-Version: 2.1
Name: TensorImage
Version: 2.1.0
Summary: Image classification library for easily and quickly deploying models and training classifiers
Home-page: https://github.com/TensorImage/TensorImage
Author: TensorImage
Author-email: tensor.image2@gmail.com
License: UNKNOWN
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        [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
        [![GitHub release](https://img.shields.io/github/release/tensorimage/tensorimage.svg)](https://GitHub.com/tensorimage/tensorimage/releases/)
        
        # TensorImage
        ![Example](https://cdn-images-1.medium.com/max/1600/1*PAqzvCxPjpDN8RC9HQw45w.jpeg) 
        
        
        __TensorImage__ is an open source library designed to make training and deploying image classification models easy.
        
        ## Features
        - 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
        
        - Multithreaded training: by default, all training operations are run in 10 threads to make training models faster
        
        - Built-in image data augmentation operations, which can be used for feature engineering:
            - Image flipping
            - Salt-pepper noise
            - Random brightness
            - Random contrast
            - Random hue
            - Random saturation
            - Gaussian blur
            - Colour filtering
        
        - 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
        
        - Large-scale image classification: deploy trained models on thousands of images, with predictions for all images being stored in your workspace directory
        
        ## Upcoming features
        - More data augmentation operations to apply on images:
            - Affine/perspective transformations
            - Random zooming
            - Random cropping
            - Individual pepper and salt noise
            - More image blurring techniques:
                - Median blur
                - Average blur
                - Motion blur
                - Bilateral blur
            - Translation
            
            
        - 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
        
        - Model inference for individual/batches of images for real-time prediction without writing on disk
        
        - 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) 
        
        ## Installation
        From the terminal:
        ```shell
        $ pip3 install tensorimage
        ```
        
        ## Documentation
        You can view TensorImage's documentation [here](https://tensorimage.readthedocs.io/en/latest/).
        
        ## Support
        If 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
        
        ## Contributing
        If 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).
        
        ## License
        TensorImage is licensed under the [MIT](https://github.com/TensorImage/tensorimage/blob/master/LICENSE.md) license.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: POSIX
Description-Content-Type: text/markdown
