diff --git a/ultralytics/utils/callbacks/clearml.py b/ultralytics/utils/callbacks/clearml.py index e076e55fa74..05c518d0b60 100644 --- a/ultralytics/utils/callbacks/clearml.py +++ b/ultralytics/utils/callbacks/clearml.py @@ -68,9 +68,9 @@ def on_pretrain_routine_start(trainer): PatchedMatplotlib.update_current_task(None) else: task = Task.init( - project_name=trainer.args.project or "YOLOv8", + project_name=trainer.args.project or "Ultralytics", task_name=trainer.args.name, - tags=["YOLOv8"], + tags=["Ultralytics"], output_uri=True, reuse_last_task_id=False, auto_connect_frameworks={"pytorch": False, "matplotlib": False}, diff --git a/ultralytics/utils/callbacks/comet.py b/ultralytics/utils/callbacks/comet.py index 7e90a538638..3a217c3f253 100644 --- a/ultralytics/utils/callbacks/comet.py +++ b/ultralytics/utils/callbacks/comet.py @@ -15,7 +15,7 @@ # Ensures certain logging functions only run for supported tasks COMET_SUPPORTED_TASKS = ["detect"] - # Names of plots created by YOLOv8 that are logged to Comet + # Names of plots created by Ultralytics that are logged to Comet EVALUATION_PLOT_NAMES = "F1_curve", "P_curve", "R_curve", "PR_curve", "confusion_matrix" LABEL_PLOT_NAMES = "labels", "labels_correlogram" @@ -31,8 +31,8 @@ def _get_comet_mode(): def _get_comet_model_name(): - """Returns the model name for Comet from the environment variable 'COMET_MODEL_NAME' or defaults to 'YOLOv8'.""" - return os.getenv("COMET_MODEL_NAME", "YOLOv8") + """Returns the model name for Comet from the environment variable COMET_MODEL_NAME or defaults to 'Ultralytics'.""" + return os.getenv("COMET_MODEL_NAME", "Ultralytics") def _get_eval_batch_logging_interval(): @@ -110,7 +110,7 @@ def _fetch_trainer_metadata(trainer): def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad): """ - YOLOv8 resizes images during training and the label values are normalized based on this resized shape. + YOLO resizes images during training and the label values are normalized based on this resized shape. This function rescales the bounding box labels to the original image shape. """ diff --git a/ultralytics/utils/callbacks/mlflow.py b/ultralytics/utils/callbacks/mlflow.py index ef449b66095..8e2d30f9030 100644 --- a/ultralytics/utils/callbacks/mlflow.py +++ b/ultralytics/utils/callbacks/mlflow.py @@ -71,7 +71,7 @@ def on_pretrain_routine_end(trainer): mlflow.set_tracking_uri(uri) # Set experiment and run names - experiment_name = os.environ.get("MLFLOW_EXPERIMENT_NAME") or trainer.args.project or "/Shared/YOLOv8" + experiment_name = os.environ.get("MLFLOW_EXPERIMENT_NAME") or trainer.args.project or "/Shared/Ultralytics" run_name = os.environ.get("MLFLOW_RUN") or trainer.args.name mlflow.set_experiment(experiment_name) diff --git a/ultralytics/utils/callbacks/neptune.py b/ultralytics/utils/callbacks/neptune.py index 6be8a821f5d..978a445bd20 100644 --- a/ultralytics/utils/callbacks/neptune.py +++ b/ultralytics/utils/callbacks/neptune.py @@ -52,7 +52,11 @@ def on_pretrain_routine_start(trainer): """Callback function called before the training routine starts.""" try: global run - run = neptune.init_run(project=trainer.args.project or "YOLOv8", name=trainer.args.name, tags=["YOLOv8"]) + run = neptune.init_run( + project=trainer.args.project or "Ultralytics", + name=trainer.args.name, + tags=["Ultralytics"], + ) run["Configuration/Hyperparameters"] = {k: "" if v is None else v for k, v in vars(trainer.args).items()} except Exception as e: LOGGER.warning(f"WARNING ⚠️ NeptuneAI installed but not initialized correctly, not logging this run. {e}") diff --git a/ultralytics/utils/callbacks/wb.py b/ultralytics/utils/callbacks/wb.py index 7b69b7a45a8..7b6d00cfc39 100644 --- a/ultralytics/utils/callbacks/wb.py +++ b/ultralytics/utils/callbacks/wb.py @@ -109,7 +109,7 @@ def _log_plots(plots, step): def on_pretrain_routine_start(trainer): """Initiate and start project if module is present.""" - wb.run or wb.init(project=trainer.args.project or "YOLOv8", name=trainer.args.name, config=vars(trainer.args)) + wb.run or wb.init(project=trainer.args.project or "Ultralytics", name=trainer.args.name, config=vars(trainer.args)) def on_fit_epoch_end(trainer):