From 39af85625e8f8f1caec1e4a69eaba3952cb61856 Mon Sep 17 00:00:00 2001
From: GuoQing Liu <842607283@qq.com>
Date: Mon, 15 Dec 2025 23:59:13 +0800
Subject: [PATCH] feat: add train classification download weights file endpoint
 (#21294)

* feat: add train classification download weights file endpoint: "TF_KERAS_MOBILENET_V2_ENDPOINT"

* refactor: custom weights file url
---
 frigate/util/classification.py | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/frigate/util/classification.py b/frigate/util/classification.py
index 1f4213315..03229cc73 100644
--- a/frigate/util/classification.py
+++ b/frigate/util/classification.py
@@ -22,6 +22,7 @@ from frigate.const import (
 from frigate.log import redirect_output_to_logger
 from frigate.models import Event, Recordings, ReviewSegment
 from frigate.types import ModelStatusTypesEnum
+from frigate.util.downloader import ModelDownloader
 from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import get_image_from_recording
 from frigate.util.process import FrigateProcess
@@ -121,6 +122,10 @@ def get_dataset_image_count(model_name: str) -> int:
 
 class ClassificationTrainingProcess(FrigateProcess):
     def __init__(self, model_name: str) -> None:
+        self.BASE_WEIGHT_URL = os.environ.get(
+            "TF_KERAS_MOBILENET_V2_WEIGHTS_URL",
+            "",
+        )
         super().__init__(
             stop_event=None,
             priority=PROCESS_PRIORITY_LOW,
@@ -179,11 +184,23 @@ class ClassificationTrainingProcess(FrigateProcess):
                 )
                 return False
 
+            weights_path = "imagenet"
+            # Download MobileNetV2 weights if not present
+            if self.BASE_WEIGHT_URL:
+                weights_path = os.path.join(
+                    MODEL_CACHE_DIR, "MobileNet", "mobilenet_v2_weights.h5"
+                )
+                if not os.path.exists(weights_path):
+                    logger.info("Downloading MobileNet V2 weights file")
+                    ModelDownloader.download_from_url(
+                        self.BASE_WEIGHT_URL, weights_path
+                    )
+
             # Start with imagenet base model with 35% of channels in each layer
             base_model = MobileNetV2(
                 input_shape=(224, 224, 3),
                 include_top=False,
-                weights="imagenet",
+                weights=weights_path,
                 alpha=0.35,
             )
             base_model.trainable = False  # Freeze pre-trained layers