user_config.yaml

general:
  model_path: ../pretrained_models/movenet/Public_pretrainedmodel_custom_dataset/custom_dataset_person_17kpts/movenet_lightning_heatmaps_192/movenet_lightning_heatmaps_192.h5
  model_type: heatmaps_spe  # spe, yolo_mpe, heatmaps_spe
  num_threads_tflite: 8
  # gpu_memory_limit: 8

operation_mode: chain_eqeb
#choices=['evaluation', 'quantization', 'benchmarking',
#         'chain_eqe','chain_qb','chain_eqeb']

dataset:
  keypoints: 17
  test_path: ../datasets/coco_val_single_pose
  quantization_path: ../datasets/coco_train_single_pose
  quantization_split: 0.3

preprocessing:
  rescaling: { scale: 1/127.5, offset: -1 }
  resizing:
    aspect_ratio: fit
    interpolation: nearest
  color_mode: rgb

postprocessing:
  kpts_conf_thresh: 0.15
  # confidence_thresh: 0.001
  # NMS_thresh: 0.1
  # plot_metrics: true
  # max_detection_boxes: 100

training:
  batch_size: 32

quantization:
  quantizer: TFlite_converter #  TFlite_converter or onnx_quantizer
  quantization_type: PTQ
  quantization_input_type: uint8
  quantization_output_type: float
  export_dir: quantized_models
  # optimize: True
  # granularity: per_tensor

prediction:
  test_files_path: ../datasets/coco_test_single_pose

tools:
   stedgeai:
      version: 9.1.0
      optimization: balanced
      on_cloud: True
      path_to_stedgeai: C:/Users/<XXXXX>/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/<*.*.*>/Utilities/windows/stedgeai.exe
   path_to_cubeIDE: C:/ST/STM32CubeIDE_<*.*.*>/STM32CubeIDE/stm32cubeide.exe

benchmarking: # valid options are STM32MP257F-EV1,STM32MP157F-DK2,STM32MP135F-DK
   board: STM32MP257F-EV1

mlflow:
  uri: ./experiments_outputs/mlruns

hydra:
  run:
    dir: ./experiments_outputs/${now:%Y_%m_%d_%H_%M_%S}