Classification Model UI (#18571)

* Setup basic training structure

* Build out route

* Handle model configs

* Add image fetch APIs

* Implement model training screen with dataset selection

* Implement viewing of training images

* Adjust directories

* Implement viewing of images

* Add support for deleting images

* Implement full deletion

* Implement classification model training

* Improve naming

* More renaming

* Improve layout

* Reduce logging

* Cleanup

frigate/util/classification.py

@@ -1,5 +1,6 @@
 """Util for classification models."""
 
+import logging
 import os
 
 import cv2
@@ -9,6 +10,8 @@ from tensorflow.keras import layers, models, optimizers
 from tensorflow.keras.applications import MobileNetV2
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
+from frigate.const import CLIPS_DIR, MODEL_CACHE_DIR
+
 BATCH_SIZE = 16
 EPOCHS = 50
 LEARNING_RATE = 0.001
@@ -35,9 +38,10 @@ def generate_representative_dataset_factory(dataset_dir: str):
 
 
 @staticmethod
-def train_classification_model(model_dir: str) -> bool:
+def train_classification_model(model_name: str) -> bool:
     """Train a classification model."""
-    dataset_dir = os.path.join(model_dir, "dataset")
+    dataset_dir = os.path.join(CLIPS_DIR, model_name, "dataset")
+    model_dir = os.path.join(MODEL_CACHE_DIR, model_name)
     num_classes = len(
         [
             d
@@ -46,6 +50,8 @@ def train_classification_model(model_dir: str) -> bool:
         ]
     )
 
+    tf.get_logger().setLevel(logging.ERROR)
+
     # Start with imagenet base model with 35% of channels in each layer
     base_model = MobileNetV2(
         input_shape=(224, 224, 3),