Improve model training and creation process

frigate/api/app.py

@@ -387,27 +387,28 @@ def config_set(request: Request, body: AppConfigSetBody):
         old_config: FrigateConfig = request.app.frigate_config
         request.app.frigate_config = config
 
-        if body.update_topic and body.update_topic.startswith("config/cameras/"):
+        if body.update_topic:
-            _, _, camera, field = body.update_topic.split("/")
+            if body.update_topic.startswith("config/cameras/"):
+                _, _, camera, field = body.update_topic.split("/")
 
-            if field == "add":
+                if field == "add":
-                settings = config.cameras[camera]
+                    settings = config.cameras[camera]
-            elif field == "remove":
+                elif field == "remove":
-                settings = old_config.cameras[camera]
+                    settings = old_config.cameras[camera]
-            else:
+                else:
-                settings = config.get_nested_object(body.update_topic)
+                    settings = config.get_nested_object(body.update_topic)
 
-            request.app.config_publisher.publish_update(
+                request.app.config_publisher.publish_update(
-                CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
+                    CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
-                settings,
+                    settings,
-            )
-        elif body.update_topic and "/config/" in body.update_topic[1:]:
-            # Handle nested config updates (e.g., config/classification/custom/{name})
-            settings = config.get_nested_object(body.update_topic)
-            if settings:
-                request.app.config_publisher.publisher.publish(
-                    body.update_topic, settings
                 )
+            else:
+                # Handle nested config updates (e.g., config/classification/custom/{name})
+                settings = config.get_nested_object(body.update_topic)
+                if settings:
+                    request.app.config_publisher.publisher.publish(
+                        body.update_topic, settings
+                    )
 
     return JSONResponse(
         content=(

frigate/api/classification.py

@@ -3,7 +3,9 @@
 import datetime
 import logging
 import os
+import random
 import shutil
+import string
 from typing import Any
 
 import cv2
@@ -707,7 +709,9 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
             status_code=404,
         )
 
-    new_name = f"{category}-{datetime.datetime.now().timestamp()}.png"
+    random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
+    timestamp = datetime.datetime.now().timestamp()
+    new_name = f"{category}-{timestamp}-{random_id}.png"
     new_file_folder = os.path.join(
         CLIPS_DIR, sanitize_filename(name), "dataset", category
     )

frigate/data_processing/real_time/custom_classification.py

@@ -353,6 +353,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             )
 
     def handle_request(self, topic, request_data):
+        logger.info(f"comparing with topic {topic} and the {request_data.get('model_name')} and the {self.model_config.name}")
         if topic == EmbeddingsRequestEnum.reload_classification_model.value:
             if request_data.get("model_name") == self.model_config.name:
                 logger.info(

frigate/embeddings/maintainer.py

@@ -286,16 +286,11 @@ class EmbeddingMaintainer(threading.Thread):
         topic, model_config = self.classification_config_subscriber.check_for_update()
 
         if topic and model_config:
-            # Extract model name from topic: config/classification/custom/{model_name}
             model_name = topic.split("/")[-1]
-            logger.info(
-                f"Received classification config update for model: {model_name}"
-            )
-
             self.config.classification.custom[model_name] = model_config
-            existing_processor_index = None
 
-            for i, processor in enumerate(self.realtime_processors):
+            # Check if processor already exists
+            for processor in self.realtime_processors:
                 if isinstance(
                     processor,
                     (
@@ -304,14 +299,10 @@ class EmbeddingMaintainer(threading.Thread):
                     ),
                 ):
                     if processor.model_config.name == model_name:
-                        existing_processor_index = i
+                        logger.debug(
-                        break
+                            f"Classification processor for model {model_name} already exists, skipping"
-
+                        )
-            if existing_processor_index is not None:
+                        return
-                logger.info(
-                    f"Removing existing classification processor for model: {model_name}"
-                )
-                self.realtime_processors.pop(existing_processor_index)
 
             if model_config.state_config is not None:
                 processor = CustomStateClassificationProcessor(
@@ -326,7 +317,9 @@ class EmbeddingMaintainer(threading.Thread):
                 )
 
             self.realtime_processors.append(processor)
-            logger.info(f"Added classification processor for model: {model_name}")
+            logger.info(
+                f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
+            )
 
     def _process_requests(self) -> None:
         """Process embeddings requests"""

web/public/locales/en/views/classificationModel.json

@@ -101,6 +101,10 @@
         "title": "Generating Sample Images",
         "description": "We're pulling representative images from your recordings. This may take a moment..."
       },
+      "training": {
+        "title": "Training Model",
+        "description": "Your model is being trained in the background. You can close this wizard and the training will continue."
+      },
       "retryGenerate": "Retry Generation",
       "selectClass": "Select class...",
       "none": "None",

web/src/components/classification/wizard/Step3ChooseExamples.tsx

@@ -46,6 +46,7 @@ export default function Step3ChooseExamples({
   const [imageClassifications, setImageClassifications] = useState<{
     [imageName: string]: string;
   }>(initialData?.imageClassifications || {});
+  const [isTraining, setIsTraining] = useState(false);
 
   const { data: trainImages, mutate: refreshTrainImages } = useSWR<string[]>(
     hasGenerated ? `classification/${step1Data.modelName}/train` : null,
@@ -186,25 +187,25 @@ export default function Step3ChooseExamples({
       });
 
       // Step 2: Classify each image by moving it to the correct category folder
-      for (const [imageName, className] of Object.entries(
+      const categorizePromises = Object.entries(imageClassifications).map(
-        imageClassifications,
+        ([imageName, className]) => {
-      )) {
+          if (!className) return Promise.resolve();
-        if (!className) continue;
+          return axios.post(
-
+            `/classification/${step1Data.modelName}/dataset/categorize`,
-        await axios.post(
+            {
-          `/classification/${step1Data.modelName}/dataset/categorize`,
+              training_file: imageName,
-          {
+              category: className === "none" ? "none" : className,
-            training_file: imageName,
+            },
-            category: className === "none" ? "none" : className,
+          );
-          },
+        },
-        );
+      );
-      }
+      await Promise.all(categorizePromises);
 
       // Step 3: Kick off training
       await axios.post(`/classification/${step1Data.modelName}/train`);
 
       toast.success(t("wizard.step3.trainingStarted"));
-      onClose();
+      setIsTraining(true);
     } catch (error) {
       const axiosError = error as {
         response?: { data?: { message?: string; detail?: string } };
@@ -220,7 +221,7 @@ export default function Step3ChooseExamples({
         t("wizard.step3.errors.classifyFailed", { error: errorMessage }),
       );
     }
-  }, [onClose, imageClassifications, step1Data, step2Data, t]);
+  }, [imageClassifications, step1Data, step2Data, t]);
 
   const allImagesClassified = useMemo(() => {
     if (!unknownImages || unknownImages.length === 0) return false;
@@ -230,7 +231,22 @@ export default function Step3ChooseExamples({
 
   return (
     <div className="flex flex-col gap-6">
-      {isGenerating ? (
+      {isTraining ? (
+        <div className="flex flex-col items-center gap-6 py-12">
+          <ActivityIndicator className="size-12" />
+          <div className="text-center">
+            <h3 className="mb-2 text-lg font-medium">
+              {t("wizard.step3.training.title")}
+            </h3>
+            <p className="text-sm text-muted-foreground">
+              {t("wizard.step3.training.description")}
+            </p>
+          </div>
+          <Button onClick={onClose} variant="select" className="mt-4">
+            {t("button.close", { ns: "common" })}
+          </Button>
+        </div>
+      ) : isGenerating ? (
         <div className="flex h-[50vh] flex-col items-center justify-center gap-4">
           <ActivityIndicator className="size-12" />
           <div className="text-center">
@@ -321,20 +337,22 @@ export default function Step3ChooseExamples({
         </div>
       )}
 
-      <div className="flex flex-col gap-3 pt-3 sm:flex-row sm:justify-end sm:gap-4">
+      {!isTraining && (
-        <Button type="button" onClick={onBack} className="sm:flex-1">
+        <div className="flex flex-col gap-3 pt-3 sm:flex-row sm:justify-end sm:gap-4">
-          {t("button.back", { ns: "common" })}
+          <Button type="button" onClick={onBack} className="sm:flex-1">
-        </Button>
+            {t("button.back", { ns: "common" })}
-        <Button
+          </Button>
-          type="button"
+          <Button
-          onClick={handleContinue}
+            type="button"
-          variant="select"
+            onClick={handleContinue}
-          className="flex items-center justify-center gap-2 sm:flex-1"
+            variant="select"
-          disabled={!hasGenerated || isGenerating || !allImagesClassified}
+            className="flex items-center justify-center gap-2 sm:flex-1"
-        >
+            disabled={!hasGenerated || isGenerating || !allImagesClassified}
-          {t("button.continue", { ns: "common" })}
+          >
-        </Button>
+            {t("button.continue", { ns: "common" })}
-      </div>
+          </Button>
+        </div>
+      )}
     </div>
   );
 }

web/src/views/classification/ModelSelectionView.tsx

@@ -30,9 +30,12 @@ export default function ModelSelectionView({
   const { t } = useTranslation(["views/classificationModel"]);
   const [page, setPage] = useState<ModelType>("objects");
   const [pageToggle, setPageToggle] = useOptimisticState(page, setPage, 100);
-  const { data: config } = useSWR<FrigateConfig>("config", {
+  const { data: config, mutate: refreshConfig } = useSWR<FrigateConfig>(
-    revalidateOnFocus: false,
+    "config",
-  });
+    {
+      revalidateOnFocus: false,
+    },
+  );
 
   // data
 
@@ -71,7 +74,10 @@ export default function ModelSelectionView({
       <>
         <ClassificationModelWizardDialog
           open={newModel}
-          onClose={() => setNewModel(false)}
+          onClose={() => {
+            setNewModel(false);
+            refreshConfig();
+          }}
         />
         <NoModelsView onCreateModel={() => setNewModel(true)} />;
       </>
@@ -82,7 +88,10 @@ export default function ModelSelectionView({
     <div className="flex size-full flex-col p-2">
       <ClassificationModelWizardDialog
         open={newModel}
-        onClose={() => setNewModel(false)}
+        onClose={() => {
+          setNewModel(false);
+          refreshConfig();
+        }}
       />
 
       <div className="flex h-12 w-full items-center justify-between">