common:
    target_module: 'vision'
    task_type: 'detection'
    target_device: 'AM62A'
    # run_name can be any string, but there are some special cases:
    # {date-time} will be replaced with datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
    # {model_name} will be replaced with the name of the model
    run_name: '{date-time}/{model_name}'

dataset:
    # enable/disable dataset loading
    enable: True #False
    # max_num_files: [750, 250] #None

    # Object Detection Dataset Examples:
    # -------------------------------------
    # Example 1, (known datasets): 'widerface_detection', 'pascal_voc0712', 'coco_detection', 'udacity_selfdriving', 'tomato_detection', 'tiscapes2017_driving'
    # dataset_name: widerface_detection
    # -------------------------------------
    # Example 2, give a dataset name and input_data_path.
    # input_data_path could be a path to zip file, tar file, folder OR http, https link to zip or tar files
    # for input_data_path these are provided with this repository as examples:
    #    'http://software-dl.ti.com/jacinto7/esd/modelzoo/08_06_00_01/datasets/tiscapes2017_driving.zip'
    #    'http://software-dl.ti.com/jacinto7/esd/modelzoo/08_06_00_01/datasets/animal_detection.zip'
    # -------------------------------------
    # Example 3, give image folders with annotation files (require list with values for both train and val splits)
    # dataset_name: coco_detection
    # input_data_path: ["./data/projects/coco_detection/dataset/train2017",
    #                        "./data/projects/coco_detection/dataset/val2017"]
    # input_annotation_path: ["./data/projects/coco_detection/dataset/annotations/instances_train2017.json",
    #                        "./data/projects/coco_detection/dataset/annotations/instances_val2017.json"]
    # -------------------------------------
    # dataset_name: htt_Fan_Mat_basic_cam.v2i.coco
    # input_data_path: ["./data/projects/htt_Fan_Mat_basic_cam.v2i.coco/dataset/train",
    #                        "./data/projects/htt_Fan_Mat_basic_cam.v2i.coco/dataset/valid"]
    # input_annotation_path: ["./data/projects/htt_Fan_Mat_basic_cam.v2i.coco/dataset/annotations/instances_train_4897.json", 
    #                        "./data/projects/htt_Fan_Mat_basic_cam.v2i.coco/dataset/annotations/instances_val_4897.json"]
    # dataset_name: tiscapes2017_driving
    # input_data_path: 'http://software-dl.ti.com/jacinto7/esd/modelzoo/08_06_00_01/datasets/tiscapes2017_driving.zip'

    dataset_name: htt_Fan_Matt_basic_cam.v3i.coco_modified
    input_data_path: '/local_data/home/mattmak/htt_Fan_Matt_basic_cam.v3i.coco_modified.zip'
    split_factor: 0.9

training:
    # enable/disable training
    enable: True #False
    find_unused_parameters: True
    num_workers: 4
    num_classes: 6


    # Object Detection model chosen can be changed here if needed
    # options are: 'yolox_s_lite', 'yolox_tiny_lite', 'yolox_nano_lite', 'yolox_pico_lite', 'yolox_femto_lite'
    model_name: 'yolox_s_lite'

    training_epochs: 100 #30
    batch_size: 12 #32
    learning_rate: 0.01
    # distributed: False   #if distribted: False --> model training in cpu not gpu and idk why
    num_gpus: 1 #1 #4

compilation:
    # enable/disable compilation
    enable: True #False
    tensor_bits: 16 #16 #32

    calibration_frames: 400
    calibration_iterations: 100

    detection_threshold: 0.1
    detection_top_k: 500