super-gradients/src/super_gradients/recipes/coco2017_ssd_lite_mobilenet_v2.yaml

# SSD MobileNetV2 Detection training on CoCo2017 Dataset:
# Trained in 320x320 mAP@0.5@0.95 (COCO API, confidence 0.001, IoU threshold 0.6, test on 320x320 images) ~20.52
# Checkpoint path: https://deci-pretrained-models.s3.amazonaws.com/ssd_lite_mobilenet_v2_coco_res320_new_coco_filtered_affine_scale_5_15_no_mosaic/ckpt_best.pth
# (trained with stride_16_plus_big)
# Hardware: 8 NVIDIA RTX 3090
# Training time: ±17 hours
#


# Instructions:
#   0. Make sure that the data is stored in dataset_params.dataset_dir or add "dataset_params.data_dir=<PATH-TO-DATASET>" at the end of the command below (feel free to check ReadMe)
#   1. Move to the project root (where you will find the ReadMe and src folder)
#   2. Run the command:
#       python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_ssd_lite_mobilenet_v2


# NOTE:
# Anchors will be selected based on validation resolution and anchors_name
# To switch between anchors, set anchors_name to something else defined in the anchors section
# e.g.
# python src/super_gradients/examples/train_from_recipe_example/train_from_recipe.py --config-name=coco2017_ssd_lite_mobilenet_v2 anchors_name=stride_16_plus


defaults:
  - training_hyperparams: coco2017_ssd_lite_mobilenet_v2_train_params
  - dataset_params: coco_detection_ssd_lite_mobilenet_v2_dataset_params
  - checkpoint_params: default_checkpoint_params
  - anchors: ssd_anchors

train_dataloader: coco2017_train
val_dataloader: coco2017_val

architecture: ssd_lite_mobilenet_v2

data_loader_num_workers: 8

experiment_suffix: res${dataset_params.train_image_size}
experiment_name: ${architecture}_coco_${experiment_suffix}

anchors_resolution: ${dataset_params.val_image_size}x${dataset_params.val_image_size}
anchors_name: stride_16_plus_big
dboxes: ${anchors.${anchors_resolution}.${anchors_name}}

arch_params:
  num_classes: 80
  anchors: ${dboxes}

resume: False
training_hyperparams:
  resume: ${resume}
  criterion_params:
    alpha: 1.0
    dboxes: ${dboxes}

multi_gpu: DDP
num_gpus: 8

ckpt_root_dir:

# THE FOLLOWING PARAMS ARE DIRECTLY USED BY HYDRA
hydra:
  run:
    # Set the output directory (i.e. where .hydra folder that logs all the input params will be generated)
    dir: ${hydra_output_dir:${ckpt_root_dir}, ${experiment_name}}