general:
  project_name: COCO_2017_person_Demo
  model_type: st_ssd_mobilenet_v1
  model_path: ../../models/st_ssd_mobilenet_v1_025_256_int8.tflite #../pretrained_models/st_ssd_mobilenet_v1/ST_pretrainedmodel_public_dataset/coco_2017_person/st_ssd_mobilenet_v1_025_256/st_ssd_mobilenet_v1_025_256_int8.tflite
  logs_dir: logs
  saved_models_dir: saved_models
  gpu_memory_limit: 16
  global_seed: 127

operation_mode: deployment
#choices=['training' , 'evaluation', 'deployment', 'quantization', 'benchmarking',
#        'chain_tqeb','chain_tqe','chain_eqe','chain_qb','chain_eqeb','chain_qd ']

dataset:
  name: COCO_2017_person
  class_names: [ person ]
  training_path:
  validation_path:
  test_path:
  quantization_path:
  quantization_split: 0.3

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

data_augmentation:
  rotation: 30
  shearing: 15
  translation: 0.1
  vertical_flip: 0.5
  horizontal_flip: 0.2
  gaussian_blur: 3.0
  linear_contrast: [ 0.75, 1.5 ]

training:
  model:
    alpha: 0.25
    input_shape: (256, 256, 3)
    pretrained_weights: imagenet
  dropout:
  batch_size: 64
  epochs: 1000
  optimizer:
    Adam:
      learning_rate: 0.001
  callbacks:
    ReduceLROnPlateau:
      monitor: val_loss
      patience: 20
    EarlyStopping:
      monitor: val_loss
      patience: 40

postprocessing:
  confidence_thresh: 0.6
  NMS_thresh: 0.5
  IoU_eval_thresh: 0.3
  plot_metrics: True   # Plot precision versus recall curves. Default is False.
  max_detection_boxes: 10

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

benchmarking:
  board: STM32H747I-DISCO

tools:
  stm32ai:
    version: 8.1.0
    optimization: balanced
    on_cloud: False
    path_to_stm32ai: /home/darshan/Downloads/en.x-cube-ai-linux_v8.1.0/stm32ai-linux-8.1.0/linux/stm32ai
  path_to_cubeIDE: /opt/st/stm32cubeide_1.10.1/stm32cubeide

deployment:
  c_project_path: ../../stm32ai_application_code/object_detection/
  IDE: GCC
  verbosity: 1 n
  hardware_setup:
    serie: STM32H7
    board: STM32H747I-DISCO


mlflow:
  uri: ./experiments_outputs/mlruns

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