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


  
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            # Shelfnet34_lw recipe for COCO segmentation 21 classes from PASCAL.
# Reaches ~65.1 mIOU
# Trained using 4 X 2080 Ti using DDP- takes ~ 2d 7h with batch size of 8 and batch accumulate of 3 (i.e effective batch
# size is 4*8*3 = 96)
# Logs and tensorboards: s3://deci-pretrained-models/shelfnet34_coco_segmentation_tensorboard/


# 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 -m super_gradients.train_from_recipe --config-name=coco_segmentation_shelfnet_lw


# /!\ THIS RECIPE IS NOT SUPPORTED AT THE MOMENT /!\

defaults:
  - training_hyperparams: coco_segmentation_shelfnet_lw_train_params
  - dataset_params: coco_segmentation_dataset_params
  - arch_params: shelfnet34_lw_arch_params
  - checkpoint_params: default_checkpoint_params
  - _self_
  - variable_setup

train_dataloader: coco_segmentation_train
val_dataloader: coco_segmentation_val

checkpoint_params:
  strict_load: True
  load_backbone: False
  checkpoint_path:

resume: False
training_hyperparams:
  resume: ${resume}

experiment_name: coco_segmentation_21_subclass_shelfnet34

multi_gpu: DDP
num_gpus: 4


architecture: shelfnet34_lw