chain_tqeb_config.yaml

general:
  project_name: my_project
  model_path: 
  logs_dir: logs
  saved_models_dir: saved_models
  display_figures: True
  global_seed: 127
  gpu_memory_limit: 3

operation_mode: chain_tqeb

dataset:
   name: flowers
   class_names: [daisy, dandelion, roses, sunflowers, tulips]
   training_path: ../datasets/flower_photos
   validation_split: 0.15
   quantization_split: 0.3

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

data_augmentation:
  random_contrast:
    factor: 0.4
  random_brightness:
    factor: 0.2
  random_flip:
    mode: horizontal_and_vertical
  random_translation:
    width_factor: 0.2
    height_factor: 0.2
  random_rotation:
    factor: 0.15
  random_zoom:
    width_factor: 0.25
    height_factor: 0.25

training:
   model:
      name: mobilenet
      version: v2
      alpha: 0.35
      pretrained_weights: None
      input_shape: (224, 224, 3)
   frozen_layers: None
   batch_size: 32
   epochs: 1
   dropout: 0.3
   optimizer:
      Adam:
         learning_rate: 0.001
   callbacks:
      ReduceLROnPlateau:
         monitor: val_accuracy
         factor: 0.5
         patience: 10
      EarlyStopping:
         monitor: val_accuracy
         patience: 40

quantization:
   quantizer: TFlite_converter
   quantization_type: PTQ
   quantization_input_type: uint8
   quantization_output_type: float
   export_dir: quantized_models


tools:
   stedgeai:
      version: 10.0.0
      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:
   board: STM32H747I-DISCO

mlflow:
   uri: ./experiments_outputs/mlruns

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