super-gradients/src/super_gradients/recipes/roboflow_yolo_nas_s.yaml

# A recipe to fine-tune YoloNAS on Roboflow datasets.
# 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_yolo_nas_s dataset_name=digits-t2eg6

defaults:
  - training_hyperparams: coco2017_yolo_nas_train_params
  - dataset_params: roboflow_detection_dataset_params
  - checkpoint_params: default_checkpoint_params
  - arch_params: yolo_nas_s_arch_params
  - _self_
  - variable_setup

train_dataloader: roboflow_train_yolox
val_dataloader: roboflow_val_yolox

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

  train_dataloader_params:
    batch_size: 16

  val_dataloader_params:
    batch_size: 16

num_classes: ${roboflow_dataset_num_classes:${dataset_name}}

architecture: yolo_nas_s
arch_params:
  num_classes: ${num_classes}


load_checkpoint: False
checkpoint_params:
  pretrained_weights: coco


result_path: # By defaults saves results in checkpoints directory
resume: False
training_hyperparams:
  resume: ${resume}
  zero_weight_decay_on_bias_and_bn: True

  lr_warmup_epochs: 3
  warmup_mode: linear_epoch_step

  initial_lr: 5e-4
  cosine_final_lr_ratio: 0.1

  optimizer_params:
    weight_decay: 0.0001

  ema: True
  ema_params:
    decay: 0.9

  max_epochs: 100
  mixed_precision: True
  criterion_params:
    num_classes: ${num_classes}


  phase_callbacks: []
  loss:
    ppyoloe_loss:
      num_classes: ${num_classes}
      reg_max: 16

  valid_metrics_list:
    - DetectionMetrics_050:
        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

  metric_to_watch: 'mAP@0.50'

multi_gpu: Off
num_gpus: 1

experiment_suffix: ""
experiment_name: ${architecture}_roboflow_${dataset_name}${experiment_suffix}