knowledge distillation to object detection(YOLONAS)

💡 Your `Question`

i want to train knowledge distillation to object detection but failed can you guide me to train? i think object detection is different for image classification in cross entropy loss

my code is this

import torch

print(f'{torch.cuda.device_count()} GPU found: {torch.cuda.get_device_name("cuda")}')

from super_gradients import Trainer from super_gradients.training import MultiGPUMode from super_gradients.training import KDTrainer

CHECKPOINT_DIR = './' # Local path trainer = Trainer(experiment_name='transfer_learning_object_detection_yolonas', ckpt_root_dir=CHECKPOINT_DIR) experiment_name = "kd_coco_yolonas"

kd_trainer = KDTrainer(experiment_name=experiment_name, ckpt_root_dir=CHECKPOINT_DIR)

num_classes = 80

from super_gradients.training.dataloaders import coco2017_train, coco2017_val

train_dataloader = coco2017_train(dataloader_params={"batch_size":16, "shuffle":True}, dataset_params={"data_dir": "c:/Users/user/PycharmProjects/pythonProject/datasets/coco"})

val_dataloader = coco2017_val(dataloader_params={"batch_size":16, "shuffle":True}, dataset_params={"data_dir": "c:/Users/user/PycharmProjects/pythonProject/datasets/coco"})

from super_gradients.training import models from super_gradients.common.object_names import Models

student_model = models.get(Models.YOLO_NAS_S, pretrained_weights="coco", num_classes=num_classes) teacher_model = models.get(Models.YOLO_NAS_L, pretrained_weights="coco", num_classes=num_classes)

print('Num classes in the model:', student_model.num_classes)

from super_gradients.training import training_hyperparams from super_gradients.training.losses import KDLogitsLoss, CrossEntropyLoss from torchvision import transforms from multiprocessing import freeze_support

train_params = training_hyperparams.get('coco2017_yolo_nas_s') train_params['max_epochs'] = 5 train_params['lr_warmup_epochs'] = 0 train_params['lr_cooldown_epochs'] = 0 train_params['criterion_params']['num_classes'] = num_classes train_params['average_best_models'] = False train_params['initial_lr'] = 0.0005 train_params['cosine_final_lr_ratio'] = 0.9 train_params['mixed_precision'] = False

kd_params = { "max_epochs": 20, # We will stop after 3 epochs because it is slow to train on google collab 'lr_cooldown_epochs': 0, # We don't want to use lr cooldown since we only train for 3 epochs 'lr_warmup_epochs': 0, # We don't want to use lr warmup since we only train for 3 epochs "loss": KDLogitsLoss(distillation_loss_coeff=0.8, task_loss_fn=CrossEntropyLoss()), "loss_logging_items_names": ["Loss", "Task Loss", "Distillation Loss"]}

training_params = training_hyperparams.get('coco2017_yolo_nas_s', overriding_params=kd_params) arch_params={"teacher_input_adapter": transforms.Resize(640)}

if name == 'main': freeze_support()

kd_trainer.train(training_params=training_params,
             student=student_model,
             teacher=teacher_model,
             kd_architecture="kd_module",
             kd_arch_params=arch_params,
             train_loader=train_dataloader, valid_loader=val_dataloader)

#trainer.train(model=student_model, training_params=train_params, train_loader=train_dataloader, valid_loader=val_dataloader)

thank you if you write solution for my question

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