- datasets placeholder for the pose estimation datasets.
- deployment contains the necessary files for the deployment service.
- pretrained_models points on a collection of optimized pretrained models for different pose estimation use cases.
- src contains tools to evaluate, benchmark and quantize your model on your STM32 target.
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:
- 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, 'e' stands for evaluation, 'q' for quantization, 'b' for benchmark and 'd' for deployment on an STM32 board.
| operation_mode attribute | Operations |
|---|---|
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_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 |
The model_type attributes currently supported for the pose estimation are:
spe: These are single pose estimation models that outputs directly the keypoints positions and confidences.hand_spe: These are single hand landmarks estimation models that outputs directly the keypoints positions and confidences of the hand pose.heatmaps_spe: These are single pose estimation models that outputs heatmaps that we must post-process in order to get the keypoints positions and confidences.yolo_mpe: These are the YOLO (You Only Look Once) multiple pose estimation models from Ultralytics based on the well known Yolov8 that outputs the same tensor as in object detection but with the addition of a set of keypoints for each bbox.
Don't forget to follow our tuto below for a quick ramp up :
- How can I use my own dataset?
- How to define and train my own model?
- How can I fine tune a pretrained model on my own dataset?
- How can I check the accuracy after quantization of my model?
- How can I quickly check the performance of my model using the dev cloud?
- How can I deploy an Ultralytics Yolov8 pose estimation model?
- How can I evaluate my model on STM32N6 target?
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!