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.
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.