Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git


  
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            # Yolo v5 Detection training on CoCo2017 Dataset:
# Yolo v5s trained in 640x640 mAP@0.5-0.95 (COCO API, confidence 0.001, IoU threshold 0.6, test on 640x640 images) ~37.3
# Checkpoint path: s3://deci-model-repository-research/reproduce-yolo/yolos_new_640/ckpt_best.pth
# Hardware: 1 3090Ti GPU
#
# Instructions:
# Set the PYTHONPATH environment variable: (Replace "YOUR_LOCAL_PATH" with the path to the downloaded repo):
#   export PYTHONPATH="YOUR_LOCAL_PATH"/super_gradients/
# python3 train_from_recipe_example/train_from_recipe.py --config-name=coco2017_yolov5.yaml \
#    dataset_params.train_image_size=640 dataset_params.val_image_size=640 \
#    dataset_params.batch_size=64 dataset_params.val_batch_size=64 \
#    architecture=yolo_v5s experiment_name=yolo_v5s_coco2017_640 \
#    training_hyperparams.mixed_precision=true dataset_params.val_sample_loading_method=rectangular multi_gpu=Off


defaults:
  - training_hyperparams: coco2017_yolov5_train_params
  - dataset_params: coco_detection_dataset_params
  - arch_params: yolov5s_arch_params

dataset_interface:
  _target_: super_gradients.training.datasets.dataset_interfaces.dataset_interface.CoCoDetectionDatasetInterface
  dataset_params: ${dataset_params}

data_loader_num_workers: 8

model_checkpoints_location: local

load_checkpoint: False

architecture: yolo_v5s

experiment_name: ${architecture}_coco2017_320

multi_gpu:
  _target_: super_gradients.training.sg_model.MultiGPUMode
  value: 'DDP'

sg_model:
  _target_: super_gradients.SgModel
  experiment_name: ${experiment_name}
  multi_gpu: ${multi_gpu}

training_hyperparams:
  criterion_params:
    anchors:
      _target_: super_gradients.training.utils.detection_utils.Anchors
      anchors_list: [[10, 13, 16, 30, 33, 23],[30, 61, 62, 45, 59, 119],[116, 90, 156, 198, 373, 326]]
      strides: [8, 16, 32]  # output strides of all yolo outputs
    obj_loss_gain: 1.0    # will be scaled according to train image size
    box_loss_gain: 0.05   # will be scaled according to num output levels
    cls_loss_gain: 0.5    # will be scaled according to num classes and num output levels