object_detection

Object detection STM32 model zoo

Directory Components:

• datasets placeholder for the object detection datasets.
• deployment contains the necessary files for the deployment service.
• pretrained_models a collection of optimized pretrained models for different object detection use cases.
• src contains tools to train, evaluate, benchmark and quantize 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:

• training, chain_tqe, chain_tqeb
• quantization, chain_eqe, chain_qb
• evaluation, chain_eqeb
• benchmarking
• prediction
• deployment, chain_qd

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
training	Train a model from the variety of object detection models in the model zoo (BYOD) or your own model with the same model type (BYOM)
evaluation	Evaluate the accuracy of a float or quantized model on a test or validation dataset
quantization	Quantize a float model
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
deployment	Deploy a model on an STM32 board
chain_tqeb	Sequentially: training, quantization of trained model, evaluation of quantized model, benchmarking of quantized model
chain_tqe	Sequentially: training, quantization of trained model, evaluation of quantized model
chain_eqe	Sequentially: evaluation of a float model, quantization, evaluation of the quantized model
chain_qb	Sequentially: quantization of a float model, benchmarking of quantized model
chain_eqeb	Sequentially: evaluation of a float model, quantization, evaluation of quantized model, benchmarking of quantized model
chain_qd	Sequentially: quantization of a float model, deployment of quantized model