STEP 1
Data Generation
Generate
synthetic data.
Start by defining your use case: objects you want to detect and the real-world scene where detections happen.
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We use synthetic data as a replacement for
expensive and noisy real data. Learn more about synthetic data here.
1. Pick a category
Start by choosing a category of objects. Check our public catalogue and leverage all existing categories.
2. Create your catalogue
Next, define your private catalogue by selecting specific objects from a category. Spot your objects visually or use the search functionality.
3. Define the scene
Generate a synthetic dataset tailored to your real-life scenario. Chose from a range of pre-defined scenes or customise your own one.
STEP 2
Model Training
Train your
model.
Once the dataset is ready, you will find it under your datasets collection. It is time to add and customise a model.
We use Neural Networks that are customised for your specific Computer Vision use case and trained with your custom dataset. Learn more about our technology here.
1. Input your business constraints
Choose how fast or big your model should be. We automatically select the best model that fits your business constraints.
2. Select a deployment
option
Tell us upfront how you would like your model to be made available to you. Work with the deployment of your choice.
3. Customise the
results
Select post-processing options to convert the detections to your desired intelligent results.
STEP 3
Productionisation
Validate, deploy and monitor your model.
Finally, it is time for you to validate the model
before shipping it to the real world. Afterwards keeping an eye on the model is as easy as it gets!
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We validate the model once it is trained to ensure high standards of quality and robustness. Learn more about our approach here.
1. Validate the performance
Upload real images and check how the models performs. Unhappy? Resume the model development for further improvments.
2. Ready to deploy
Manage cloud or local deployments with a click of a button. You can export your model to a variety of industry standard formats.
3. Post-deployment maintenance
Get real-time performance statistics once deployed. Step in whenever the model needs an update.