Depth Estimation STM32 model zoo

Directory components:

• docs contains all readmes and documentation specific to the depth estimation use case.
• src contains tools to train, evaluate, benchmark, quantize and deploy your model on your STM32 target.

Quick & easy examples:

The operation_mode top-level attribute specifies the operations or the service you want to execute. This may be single operation or a set of chained operations.

You can refer to readme links below that provide typical examples of operation modes, and tutorials on specific services:

• benchmarking
• prediction

All .yaml configuration examples are located in config_file_examples folder.

The different values of the operation_mode attribute and the corresponding operations are described in the table below. In the names of the chain modes, 't' stands for training, 'e' for evaluation, 'q' for quantization, 'b' for benchmark and 'd' for deployment on an STM32 board.

operation_mode attribute	Operations
prediction	Predict the classes some images belong to using a float or quantized model
benchmarking	Benchmark a float or quantized model on an STM32 board

The model_type attribute currently supported for the depth estimation is fast_depth : depth estimation model.

You don't know where to start? You feel lost?

Don't forget to follow our tuto below for a quick ramp up :

• How can I quickly check the performance of my model using the dev cloud?

Remember that minimalistic yaml files are available here to play with specific services, and that all pre-trained models in the STM32 model zoo are provided with their configuration .yaml file used to generate them. These are very good starting points to start playing with!