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Strip model name before training

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This commit is contained in:
Nicolas Mowen 2026-01-11 09:15:50 -07:00
parent 7b5a1b7284
commit 3533578aef
1 changed files with 7 additions and 0 deletions
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7
frigate/util/classification.py
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@ -43,6 +43,7 @@ def write_training_metadata(model_name: str, image_count: int) -> None:
model_name: Name of the classification model
image_count: Number of images used in training
"""
model_name = model_name.strip()
clips_model_dir = os.path.join(CLIPS_DIR, model_name)
os.makedirs(clips_model_dir, exist_ok=True)
@ -70,6 +71,7 @@ def read_training_metadata(model_name: str) -> dict[str, any] | None:
Returns:
Dictionary with last_training_date and last_training_image_count, or None if not found
"""
model_name = model_name.strip()
clips_model_dir = os.path.join(CLIPS_DIR, model_name)
metadata_path = os.path.join(clips_model_dir, TRAINING_METADATA_FILE)
@ -95,6 +97,7 @@ def get_dataset_image_count(model_name: str) -> int:
Returns:
Total count of images across all categories
"""
model_name = model_name.strip()
dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
if not os.path.exists(dataset_dir):
@ -126,6 +129,7 @@ class ClassificationTrainingProcess(FrigateProcess):
"TF_KERAS_MOBILENET_V2_WEIGHTS_URL",
"",
)
model_name = model_name.strip()
super().__init__(
stop_event=None,
priority=PROCESS_PRIORITY_LOW,
@ -292,6 +296,7 @@ class ClassificationTrainingProcess(FrigateProcess):
def kickoff_model_training(
embeddingRequestor: EmbeddingsRequestor, model_name: str
) -> None:
model_name = model_name.strip()
requestor = InterProcessRequestor()
requestor.send_data(
UPDATE_MODEL_STATE,
@ -359,6 +364,7 @@ def collect_state_classification_examples(
model_name: Name of the classification model
cameras: Dict mapping camera names to normalized crop coordinates [x1, y1, x2, y2] (0-1)
"""
model_name = model_name.strip()
dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
# Step 1: Get review items for the cameras
@ -714,6 +720,7 @@ def collect_object_classification_examples(
model_name: Name of the classification model
label: Object label to collect (e.g., "person", "car")
"""
model_name = model_name.strip()
dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
temp_dir = os.path.join(dataset_dir, "temp")
os.makedirs(temp_dir, exist_ok=True)