Metadata-Version: 2.1
Name: visualone
Version: 0.0.17
Summary: Python API for Visual One's few shot learning framework.
Home-page: https://visualone.tech
Author: Visual One Technologies Inc.
Author-email: contact@visualone.tech
License: UNKNOWN
Description: <h1 align="center">
            <img width="100" src="https://www.visualone.tech/images/logo.png" alt="Visual One">
            <br>
        Visual One
        </h1>
        
        Visual One's few shot learning framework allows you to easily train models for complex computer vision tasks using only a few samples in two lines of code. <br>
        [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Train%20a%20computer%20vision%20model%20using%20only%20a%20few%20samples%20with%20two%20lines%20of%20code%21&url=https://www.visualone.tech&hashtags=ai,ml,machine_learning,deep_learning,computer_vision,few_shot_learning)
        
        To see some examples, visit the [examples page of our website.](https://visualone.tech/examples)
        
        ## Quickstart
        - Install the package: `pip install visualone`
        
        - Submit your email [here](https://getvisualone.com/access) to receive your public and private keys via email.
        
        - Train a model by providing some positive samples and some negative samples:
        ```python
        from visualone import vedx
        
        client = vedx.client(public_key, private_key)
        
        model = client.train(positive_samples, negative_samples)
        ```
        - Apply the trained model to a new image to do prediction:
        ```python
        client.predict(model['task_id'], image_file)
        ```
        
        ## Description of inputs & outputs
        #### Training
        ```python
        model = client.train(positive_samples, negative_samples)
        ```
        
        `positive_samples` and `negative_samples` must be either:
        - `str`: The path to a directory containing image files representing all of the positive/negative samples.<br>
        Or<br>
        - `list[str]`: The name of the individual files corresponding to the positive/negative samples. 
        
        Returns a `dict` with the following keys:<br>
        `task_id`: A unique task id generated for this task. You need to pass this to `client.predict` to do prediction.<br>
        `n_positive_samples`: The number of positive samples used for training the model.<br>
        `n_negative_samples`: The number of negative samples used for training the model.<br>
        `success`: `1` if the training was done successfully. `0` if an error occured.<br>
        `message`: A brief message describing the error if an error occurs.
        
        #### Prediction
        ```python
        client.predict(task_id, image_file)
        ```
        
        `task_id`: The unique task id corresponding to the trained model returned by `vedx.train` <br>
        `image_file`: The path to the file for which you want to do inference.
        
        Returns a `dict` with the following keys:<br>
        `task_id`: The task id corresponding to the model.<br>
        `prediction`: A boolean representing the model prediction. `True` means the model predicted a positive label for the given image. `False` means the model predicted a negative label for the given image. <br>
        `confidence`: A number between 0 and 100 representing the confidence of the model for this prediction. <br>
        `latency`: The time it took to do the prediction in millisecond.
        
        
        ### Report Bugs/Issues & Feature Request
        Please, help us improve our product by [reporting any bugs or issues](mailto:contact@visualone.tech?subject=[visualone%PyPI]%20Bug%20Report) while using visualone. Also, feel free to [let us know any feature requests.](mailto:contact@visualone.tech?subject=[visualone%PyPI]%20Feature%20Request)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
