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Introducing VEDX: GPT-3 For Vision

Empower your products with computer vision models that can learn complex tasks using only a few samples.
Set up and use our Python API in 5 minutes. Or integrate VEDX into your products easily using our REST API.

Cameras

Y Combinator
AWS
NVIDIA

A Novel Few Shot Learning Framework

Deep learning models require hundreds or thousands of samples to learn a new task. Collecting and labeling such amount of data can be prohibitively costly and time consuming.

Our brain can learn simple visual tasks, say recognize a specific animal, after seeing only a few examples. It can also learn very complex tasks such as recognizing a specific abnormality in an X-Ray image after seeing 10-20 examples.

We have built a novel Few Shot Learning framework, named VEDX, which is capable of doing just that. For simple tasks, you can train a model using only a few samples and for more complex tasks, you can train a model using 10-20 samples to achieve high accuracy.

VEDX is a versatile framework and can be used to train models for any computer vision task posed as a binary or multi-class classification problem.

Objects

Set up, train & apply in 5 min!

Train and apply a model in two lines of code using our Python API.
from visualone import vedx

client = vedx.client(public_key, private_key)

# Train a model using a few samples
model = client.train(positive_samples, negative_samples)

# Apply the trained model to a new image
client.predict(model['task_id'], image_file)
            

Install our Python package: pip install visualone
You can access the docs on our PyPI page.

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

Train a model with a few positive samples and a few negative samples in 10 sec. Then apply the model on a new image and get the result in a fraction of a second.

You can use this IPython notebook to run and evaluate the results quickly.


Integrate via Our REST API

You can use our REST API to train a model and apply it to new images.

To train a model for a new task, upload the training samples into a S3 bucket and send a POST request to our Task Creator endpoint.

To apply the trained model to a new image, upload the image into a S3 bucket and send a GET request to our Predictor endpoint.

Objects

Case Studies

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 82%
Number of samples used for training: 5,216

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
5 5 71%
25 25 81%
50 50 87%
100 100 85%

Summary:
Best accuracy achieved with much fewer samples, 50 samples vs. 5000. Significantly higher accuracy achieved (85% vs. 82%) with 200 training samples.

Pneumonia

Positive 1 Positive 2

Normal

Negative 1 Negative 2

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 96%
Number of samples used for training: 2,475

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
3 3 86%
5 5 86%
15 15 94%
25 25 100%

Summary:
Higher accuracy achieved (100% vs. 96%) with significantly lower number of training samples, 50 samples vs. ~2500 samples.

Bacterial Disease

Positive 1 Positive 2

Healthy

Negative 1 Negative 2

Dataset from Kaggle.
Best result achieved on Kaggle as reported here:
Accuracy: 98%
Number of samples used for training: 10,900

VEDX accuracy for different number of samples used for training:

n_positives n_negatives Accuracy
3 3 81%
5 5 85%
15 15 98%
25 25 98%
50 50 99%

Summary:
Same accuracy achieved using much fewer samples (30 vs 10,900.)
Good accuracy achieved using only 10 samples.

Rotten

Positive 1 Positive 2 Positive 3

Fresh

Negative 1 Negative 2 Negative 3

You can see more case studies here.


Event Detection

In this example, we train a model to detect when a door is open.
In this example, we train a model to detect when a hot tub has been left uncovered, in other words, when the hot tub cover is off and also no one is in/around the hot tub.
In this example, we train a model to detect when a faucet is left running and no one is next to it.
In this example, we train a model to detect whether or not a person is wearing a mask. We also demo how a user can provide feedback to correct the model if it makes a false prediction.

You can see more demos or run a live experiment using your webcam here.