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Python API for Visual One's few shot learning framework.

Project description

Visual One
Visual One

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.
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To see some examples, visit the examples page of our website.

Quickstart

  • Install the package: pip install visualone

  • Submit your email here to receive your public and private keys via email.

  • Train a model by providing some positive samples and some negative samples:

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:
client.predict(model['task_id'], image_file)

Description of inputs & outputs

Training

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.
    Or
  • list[str]: The name of the individual files corresponding to the positive/negative samples.

Returns a dict with the following keys:
task_id: A unique task id generated for this task. You need to pass this to client.predict to do prediction.
n_positive_samples: The number of positive samples used for training the model.
n_negative_samples: The number of negative samples used for training the model.
success: 1 if the training was done successfully. 0 if an error occured.
message: A brief message describing the error if an error occurs.

Prediction

client.predict(task_id, image_file)

task_id: The unique task id corresponding to the trained model returned by vedx.train
image_file: The path to the file for which you want to do inference.

Returns a dict with the following keys:
task_id: The task id corresponding to the model.
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.
confidence: A number between 0 and 100 representing the confidence of the model for this prediction.
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 while using visualone. Also, feel free to let us know any feature requests.

Project details


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