roboflow_ppyoloe.yaml

# Checkout the datasets at https://universe.roboflow.com/roboflow-100?ref=blog.roboflow.com
#
# `dataset_name` refers to the official name of the dataset.
# You can find it in the url of the dataset: https://universe.roboflow.com/roboflow-100/digits-t2eg6 -> digits-t2eg6
#
# Example: python -m super_gradients.train_from_recipe --config-name=roboflow_ppyoloe dataset_name=digits-t2eg6


defaults:
  - training_hyperparams: coco2017_ppyoloe_train_params
  - dataset_params: roboflow_detection_dataset_params
  - checkpoint_params: default_checkpoint_params
  - arch_params: ppyoloe_m_arch_params
  - _self_
  - variable_setup

dataset_name: ??? # Placeholder for the name of the dataset you want to use (e.g. "digits-t2eg6")
dataset_params:
  dataset_name: ${dataset_name}
num_classes: ${roboflow_dataset_num_classes:${dataset_name}}


architecture: ppyoloe_m
arch_params:
  num_classes: ${num_classes}


train_dataloader: roboflow_train_yolox
val_dataloader: roboflow_val_yolox


load_checkpoint: False
checkpoint_params:
  pretrained_weights: coco


result_path: # By defaults saves results in checkpoints directory
resume: False
training_hyperparams:
  resume: ${resume}
  max_epochs: 100
  mixed_precision: True
  criterion_params:
    num_classes: ${num_classes}
    reg_max: ${arch_params.head.reg_max}
  phase_callbacks:
    - RoboflowResultCallback:
        dataset_name: ${dataset_name}
        output_path: ${result_path}
  loss: PPYoloELoss

  valid_metrics_list:
    - DetectionMetrics:
        score_thres: 0.1
        top_k_predictions: 300
        num_cls: ${num_classes}
        normalize_targets: True
        post_prediction_callback:
          _target_: super_gradients.training.models.detection_models.pp_yolo_e.PPYoloEPostPredictionCallback
          score_threshold: 0.01
          nms_top_k: 1000
          max_predictions: 300
          nms_threshold: 0.7


multi_gpu: DDP
num_gpus:
experiment_name: ${architecture}_roboflow_${dataset_name}