(models-supergradients)= # SuperGradients ```{important} [SuperGradients](https://github.com/Deci-AI/super-gradients) must be installed with `pip install "lightly-train[super-gradients]"`. ``` ```{warning} SuperGradients support is still experimental. There might be unexpected warnings in the logs. ``` ## Pretrain and Fine-tune a SuperGradients Model ### Pretrain Pretraining a SuperGradients models with LightlyTrain is straightforward. Below we provide the minimum scripts for pretraining using `super_gradients/yolo_nas_s` as an example: ````{tab} Python ```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. ) ``` Or alternatively, pass directly a SuperGradients model instance: ```python from super_gradients.training import models import lightly_train if __name__ == "__main__": model = models.get(model_name="yolo_nas_s", num_classes=3) # Load the model. lightly_train.train( out="out/my_experiment", # Output directory. data="my_data_dir", # Directory with images. model=model, # Pass the SuperGradients model. ) ```` ````{tab} Command Line ```bash lightly-train train out="out/my_experiment" data="my_data_dir" model="super_gradients/yolo_nas_s" ```` ### Fine-tune After pretraining, you can load the exported model for fine-tuning with SuperGradients: ```python 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", ) ``` ## 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`