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Three main actions
The general concept of the One Step AI service is based on 3 main actions:
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Dataset preparation
To get started, upload your images from which you want to build a dataset. You can have multiple datasets. Freely add, delete and edit them by adding and deleting the images. You can annotate objects on images within the datasets, assign categories to them, and combine categories from multiple datasets together. You can also augment data based on the operations you choose. All the datasets you own are stored in your account.
For much more detailed information, go to the Datasets section.
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Model training
Once your datasets are ready, the next step is to create and train a model. The OneStepAI service offers classification and detection models from popular frameworks (currently Keras, PyTorch and Darknet, but more will be added). If you are not sure how to configure the parameters of the training process, use our wizard with recommended settings in Basic mode. However, if you want full control over the process, use the Advanced mode. If you are concerned about the repetitiveness of the training parameterization process, use Templates
, which you will be able to recall in subsequent training sessions (see the Model Templates section for details).
For much more detailed information, go to the Model Training section.
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Testing the model
In the last step of the process, you can test the model on edge devices. The diagram below shows how we are able to make this happen.
You can rent a device from our cloud or use your own - see the article on adding devices to the AIDWS app for more information.
We support the following types of edge devices:
- NVIDIA Jetson Nano optionally with both:
- Intel Neural Compute Stick 2 (Intel NCS2)
- Google Coral TPU USB-Accelarator
- OSAI virtual machine With GPU:
- (soon) OSAI virtual machine with CPU optionally with both:
- Intel Neural Compute Stick 2 (Intel NCS2)
- Google Coral TPU USB-Accelarator
Along with the above-mentioned devices, we offer the AIDWS Web App software to test the models created in the OSAI service.
For a more detailed overview, go to the Live Testing section.