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This repository has been archived by the owner on Mar 12, 2024. It is now read-only.
Hello can someone help me how can i continue training detr from last epoch with checkpoint
this is the code for training :
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning import Trainer
MAX_EPOCHS = 200
early_stopping_callback = EarlyStopping(
monitor='training_loss', # Monitor validation AP
min_delta=0.00, # Minimum change in AP
patience=3, # Number of epochs to wait for improvement before stopping
mode='max' # Consider AP as a maximization metric
)
Hello can someone help me how can i continue training detr from last epoch with checkpoint
this is the code for training :
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning import Trainer
MAX_EPOCHS = 200
early_stopping_callback = EarlyStopping(
monitor='training_loss', # Monitor validation AP
min_delta=0.00, # Minimum change in AP
patience=3, # Number of epochs to wait for improvement before stopping
mode='max' # Consider AP as a maximization metric
)
trainer = Trainer(
devices=1,
accelerator="gpu",
max_epochs=MAX_EPOCHS,
gradient_clip_val=0.1,
accumulate_grad_batches=8,
log_every_n_steps=5,
callbacks=[early_stopping_callback]
)
trainer.fit(model)
should i add something or what i should do next
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