SuperGradients

Important

SuperGradients must be installed with pip install "lightly-train[super-gradients]".

Warning

SuperGradients support is still experimental. There might be unexpected warnings in the logs.

Supported Models

• PP-LiteSeg

  • super_gradients/pp_lite_b_seg

  • super_gradients/pp_lite_b_seg50

  • super_gradients/pp_lite_b_seg75

  • super_gradients/pp_lite_t_seg

  • super_gradients/pp_lite_t_seg50

  • super_gradients/pp_lite_t_seg75

• SSD

  • super_gradients/ssd_lite_mobilenet_v2

  • super_gradients/ssd_mobilenet_v1

• YOLO-NAS

  • super_gradients/yolo_nas_l

  • super_gradients/yolo_nas_m

  • super_gradients/yolo_nas_pose_l

  • super_gradients/yolo_nas_pose_m

  • super_gradients/yolo_nas_pose_n

  • super_gradients/yolo_nas_pose_s

  • super_gradients/yolo_nas_s

Pretrain a SuperGradients Model

Pretraining a SuperGradients models with LightlyTrain is straightforward. Below we will provide the minimum scripts for pretraining using super_gradients/yolo_nas_s as an example:

Python

import lightly_train

if __name__ == "__main__":
    lightly_train.train(
        out="out/my_experiment",                # Output directory.
        data="my_data_dir",                     # Directory with images.
        model="super_gradients/yolo_nas_s",     # Pass the supergradient model.
    )

Command Line

lightly-train train out="out/my_experiment" data="my_data_dir" model="super_gradients/yolo_nas_s"

You can reload the exported model by super_gradients directly:

from super_gradients.training import models

model = models.get(
  model_name="yolo_nas_s",
  num_classes=3,
  checkpoint_path="out/my_experiment/exported_models/exported_last.pt",
)