Lazy loading

.cursor/rules/frontend-always-use-translation-files.mdc

@@ -0,0 +1,6 @@
+---
+globs: ["**/*.ts", "**/*.tsx"]
+alwaysApply: false
+---
+
+Never write strings in the frontend directly, always write to and reference the relevant translations file.

docs/docs/configuration/custom_classification/object_classification.md

@@ -12,7 +12,18 @@ Object classification models are lightweight and run very fast on CPU. Inference
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
 
-### Sub label vs Attribute
+## Classes
+
+Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
+
+For object classification:
+
+- Define classes that represent different types or attributes of the detected object
+- Examples: For `person` objects, classes might be `delivery_person`, `resident`, `stranger`
+- Include a `none` class for objects that don't fit any specific category
+- Keep classes visually distinct to improve accuracy
+
+### Classification Type
 
 - **Sub label**:
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -12,6 +12,17 @@ State classification models are lightweight and run very fast on CPU. Inference
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
 When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
 
+## Classes
+
+Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.
+
+For state classification:
+
+- Define classes that represent mutually exclusive states
+- Examples: `open` and `closed` for a garage door, `on` and `off` for lights
+- Use at least 2 classes (typically binary states work best)
+- Keep class names clear and descriptive
+
 ## Example use cases
 
 - **Door state**: Detect if a garage or front door is open vs closed.

frigate/api/classification.py

@@ -167,8 +167,7 @@ def train_face(request: Request, name: str, body: dict = None):
     new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
     new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
 
-    if not os.path.exists(new_file_folder):
-        os.mkdir(new_file_folder)
+    os.makedirs(new_file_folder, exist_ok=True)
 
     if training_file_name:
         shutil.move(training_file, os.path.join(new_file_folder, new_name))
@@ -716,8 +715,7 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
         CLIPS_DIR, sanitize_filename(name), "dataset", category
     )
 
-    if not os.path.exists(new_file_folder):
-        os.mkdir(new_file_folder)
+    os.makedirs(new_file_folder, exist_ok=True)
 
     # use opencv because webp images can not be used to train
     img = cv2.imread(training_file)

frigate/data_processing/real_time/custom_classification.py

@@ -53,9 +53,17 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         self.tensor_output_details: dict[str, Any] | None = None
         self.labelmap: dict[int, str] = {}
         self.classifications_per_second = EventsPerSecond()
-        self.inference_speed = InferenceSpeed(
-            self.metrics.classification_speeds[self.model_config.name]
-        )
+
+        if (
+            self.metrics
+            and self.model_config.name in self.metrics.classification_speeds
+        ):
+            self.inference_speed = InferenceSpeed(
+                self.metrics.classification_speeds[self.model_config.name]
+            )
+        else:
+            self.inference_speed = None
+
         self.last_run = datetime.datetime.now().timestamp()
         self.__build_detector()
 
@@ -83,12 +91,14 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
 
     def __update_metrics(self, duration: float) -> None:
         self.classifications_per_second.update()
-        self.inference_speed.update(duration)
+        if self.inference_speed:
+            self.inference_speed.update(duration)
 
     def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray):
-        self.metrics.classification_cps[
-            self.model_config.name
-        ].value = self.classifications_per_second.eps()
+        if self.metrics and self.model_config.name in self.metrics.classification_cps:
+            self.metrics.classification_cps[
+                self.model_config.name
+            ].value = self.classifications_per_second.eps()
         camera = frame_data.get("camera")
 
         if camera not in self.model_config.state_config.cameras:
@@ -223,9 +233,17 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
         self.detected_objects: dict[str, float] = {}
         self.labelmap: dict[int, str] = {}
         self.classifications_per_second = EventsPerSecond()
-        self.inference_speed = InferenceSpeed(
-            self.metrics.classification_speeds[self.model_config.name]
-        )
+
+        if (
+            self.metrics
+            and self.model_config.name in self.metrics.classification_speeds
+        ):
+            self.inference_speed = InferenceSpeed(
+                self.metrics.classification_speeds[self.model_config.name]
+            )
+        else:
+            self.inference_speed = None
+
         self.__build_detector()
 
     @redirect_output_to_logger(logger, logging.DEBUG)
@@ -251,12 +269,14 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
 
     def __update_metrics(self, duration: float) -> None:
         self.classifications_per_second.update()
-        self.inference_speed.update(duration)
+        if self.inference_speed:
+            self.inference_speed.update(duration)
 
     def process_frame(self, obj_data, frame):
-        self.metrics.classification_cps[
-            self.model_config.name
-        ].value = self.classifications_per_second.eps()
+        if self.metrics and self.model_config.name in self.metrics.classification_cps:
+            self.metrics.classification_cps[
+                self.model_config.name
+            ].value = self.classifications_per_second.eps()
 
         if obj_data["false_positive"]:
             return

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

@@ -1,4 +1,5 @@
 {
+  "documentTitle": "Classification Models",
   "button": {
     "deleteClassificationAttempts": "Delete Classification Images",
     "renameCategory": "Rename Class",
@@ -51,20 +52,26 @@
   "categorizeImageAs": "Classify Image As:",
   "categorizeImage": "Classify Image",
   "noModels": {
-    "title": "No Classification Models",
-    "description": "Create a custom model to classify objects or monitor state changes in your cameras.",
-    "buttonText": "Create Classification Model"
+    "object": {
+      "title": "No Object Classification Models",
+      "description": "Create a custom model to classify detected objects.",
+      "buttonText": "Create Object Model"
+    },
+    "state": {
+      "title": "No State Classification Models",
+      "description": "Create a custom model to monitor and classify state changes in specific camera areas.",
+      "buttonText": "Create State Model"
+    }
   },
   "wizard": {
     "title": "Create New Classification",
     "steps": {
       "nameAndDefine": "Name & Define",
       "stateArea": "State Area",
-      "chooseExamples": "Choose Examples",
-      "train": "Train"
+      "chooseExamples": "Choose Examples"
     },
     "step1": {
-      "description": "Create a new state or object classification model.",
+      "description": "State models monitor fixed camera areas for changes (e.g., door open/closed). Object models add classifications to detected objects (e.g., known animals, delivery persons, etc.).",
       "name": "Name",
       "namePlaceholder": "Enter model name...",
       "type": "Type",
@@ -73,9 +80,14 @@
       "objectLabel": "Object Label",
       "objectLabelPlaceholder": "Select object type...",
       "classificationType": "Classification Type",
+      "classificationTypeTip": "Learn about classification types",
+      "classificationTypeDesc": "Sub Labels add additional text to the object label (e.g., 'Person: UPS'). Attributes are searchable metadata stored separately in the object metadata.",
       "classificationSubLabel": "Sub Label",
       "classificationAttribute": "Attribute",
       "classes": "Classes",
+      "classesTip": "Learn about classes",
+      "classesStateDesc": "Define the different states your camera area can be in. For example: 'open' and 'closed' for a garage door.",
+      "classesObjectDesc": "Define the different categories to classify detected objects into. For example: 'delivery_person', 'resident', 'stranger' for person classification.",
       "classPlaceholder": "Enter class name...",
       "errors": {
         "nameRequired": "Model name is required",
@@ -96,18 +108,17 @@
       "selectCameraPrompt": "Select a camera from the list to define its monitoring area"
     },
     "step3": {
-      "description": "Classify the example images below. These samples will be used to train your model.",
+      "selectImagesPrompt": "Select all images with: {{className}}",
+      "selectImagesDescription": "Click on images to select them. Click Continue when you're done with this class.",
       "generating": {
         "title": "Generating Sample Images",
-        "description": "We're pulling representative images from your recordings. This may take a moment..."
+        "description": "Frigate is 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."
+        "description": "Your model is being trained in the background. Close this dialog, and your model will start running as soon as training is complete."
       },
       "retryGenerate": "Retry Generation",
-      "selectClass": "Select class...",
-      "none": "None",
       "noImages": "No sample images generated",
       "classifying": "Classifying & Training...",
       "trainingStarted": "Training started successfully",

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

@@ -5,10 +5,6 @@
     "invalidName": "Invalid name. Names can only include letters, numbers, spaces, apostrophes, underscores, and hyphens."
   },
   "details": {
-    "subLabelScore": "Sub Label Score",
-    "scoreInfo": "The sub label score is the weighted score for all of the recognized face confidences, so this may differ from the score shown on the snapshot.",
-    "face": "Face Details",
-    "faceDesc": "Details of the tracked object that generated this face",
     "timestamp": "Timestamp",
     "unknown": "Unknown"
   },
@@ -19,8 +15,6 @@
   },
   "collections": "Collections",
   "createFaceLibrary": {
-    "title": "Create Collection",
-    "desc": "Create a new collection",
     "new": "Create New Face",
     "nextSteps": "To build a strong foundation:<li>Use the Recent Recognitions tab to select and train on images for each detected person.</li><li>Focus on straight-on images for best results; avoid training images that capture faces at an angle.</li></ul>"
   },
@@ -37,8 +31,6 @@
     "aria": "Select recent recognitions",
     "empty": "There are no recent face recognition attempts"
   },
-  "selectItem": "Select {{item}}",
-  "selectFace": "Select Face",
   "deleteFaceLibrary": {
     "title": "Delete Name",
     "desc": "Are you sure you want to delete the collection {{name}}? This will permanently delete all associated faces."
@@ -69,7 +61,6 @@
     "maxSize": "Max size: {{size}}MB"
   },
   "nofaces": "No faces available",
-  "pixels": "{{area}}px",
   "trainFaceAs": "Train Face as:",
   "trainFace": "Train Face",
   "toast": {

web/src/components/card/ClassificationCard.tsx

@@ -126,6 +126,7 @@ export const ClassificationCard = forwardRef<
           imgClassName,
           isMobile && "w-full",
         )}
+        loading="lazy"
         onLoad={() => setImageLoaded(true)}
         src={`${baseUrl}${data.filepath}`}
       />

web/src/components/classification/ClassificationModelWizardDialog.tsx

@@ -30,6 +30,7 @@ const STATE_STEPS = [
 type ClassificationModelWizardDialogProps = {
   open: boolean;
   onClose: () => void;
+  defaultModelType?: "state" | "object";
 };
 
 type WizardState = {
@@ -92,6 +93,7 @@ function wizardReducer(state: WizardState, action: WizardAction): WizardState {
 export default function ClassificationModelWizardDialog({
   open,
   onClose,
+  defaultModelType,
 }: ClassificationModelWizardDialogProps) {
   const { t } = useTranslation(["views/classificationModel"]);
 
@@ -135,7 +137,12 @@ export default function ClassificationModelWizardDialog({
       <DialogContent
         className={cn(
           "",
-          isDesktop && "max-h-[75dvh] max-w-6xl overflow-y-auto",
+          isDesktop &&
+            wizardState.currentStep == 0 &&
+            "max-h-[90%] overflow-y-auto xl:max-h-[80%]",
+          isDesktop &&
+            wizardState.currentStep > 0 &&
+            "max-h-[90%] max-w-[70%] overflow-y-auto xl:max-h-[80%]",
         )}
         onInteractOutside={(e) => {
           e.preventDefault();
@@ -166,6 +173,7 @@ export default function ClassificationModelWizardDialog({
           {wizardState.currentStep === 0 && (
             <Step1NameAndDefine
               initialData={wizardState.step1Data}
+              defaultModelType={defaultModelType}
               onNext={handleStep1Next}
               onCancel={handleCancel}
             />

web/src/components/classification/wizard/Step1NameAndDefine.tsx

@@ -22,11 +22,16 @@ import { zodResolver } from "@hookform/resolvers/zod";
 import { z } from "zod";
 import { useTranslation } from "react-i18next";
 import { useMemo } from "react";
-import { LuX } from "react-icons/lu";
-import { MdAddBox } from "react-icons/md";
+import { LuX, LuPlus, LuInfo, LuExternalLink } from "react-icons/lu";
 import useSWR from "swr";
 import { FrigateConfig } from "@/types/frigateConfig";
 import { getTranslatedLabel } from "@/utils/i18n";
+import { useDocDomain } from "@/hooks/use-doc-domain";
+import {
+  Popover,
+  PopoverContent,
+  PopoverTrigger,
+} from "@/components/ui/popover";
 
 export type ModelType = "state" | "object";
 export type ObjectClassificationType = "sub_label" | "attribute";
@@ -41,17 +46,20 @@ export type Step1FormData = {
 
 type Step1NameAndDefineProps = {
   initialData?: Partial<Step1FormData>;
+  defaultModelType?: "state" | "object";
   onNext: (data: Step1FormData) => void;
   onCancel: () => void;
 };
 
 export default function Step1NameAndDefine({
   initialData,
+  defaultModelType,
   onNext,
   onCancel,
 }: Step1NameAndDefineProps) {
   const { t } = useTranslation(["views/classificationModel"]);
   const { data: config } = useSWR<FrigateConfig>("config");
+  const { getLocaleDocUrl } = useDocDomain();
 
   const objectLabels = useMemo(() => {
     if (!config) return [];
@@ -147,7 +155,7 @@ export default function Step1NameAndDefine({
     resolver: zodResolver(step1FormData),
     defaultValues: {
       modelName: initialData?.modelName || "",
-      modelType: initialData?.modelType || "state",
+      modelType: initialData?.modelType || defaultModelType || "state",
       objectLabel: initialData?.objectLabel,
       objectType: initialData?.objectType || "sub_label",
       classes: initialData?.classes?.length ? initialData.classes : [""],
@@ -194,7 +202,9 @@ export default function Step1NameAndDefine({
             name="modelName"
             render={({ field }) => (
               <FormItem>
-                <FormLabel>{t("wizard.step1.name")}</FormLabel>
+                <FormLabel className="text-primary-variant">
+                  {t("wizard.step1.name")}
+                </FormLabel>
                 <FormControl>
                   <Input
                     className="h-8"
@@ -212,7 +222,9 @@ export default function Step1NameAndDefine({
             name="modelType"
             render={({ field }) => (
               <FormItem>
-                <FormLabel>{t("wizard.step1.type")}</FormLabel>
+                <FormLabel className="text-primary-variant">
+                  {t("wizard.step1.type")}
+                </FormLabel>
                 <FormControl>
                   <RadioGroup
                     onValueChange={field.onChange}
@@ -261,7 +273,9 @@ export default function Step1NameAndDefine({
                 name="objectLabel"
                 render={({ field }) => (
                   <FormItem>
-                    <FormLabel>{t("wizard.step1.objectLabel")}</FormLabel>
+                    <FormLabel className="text-primary-variant">
+                      {t("wizard.step1.objectLabel")}
+                    </FormLabel>
                     <Select
                       onValueChange={field.onChange}
                       defaultValue={field.value}
@@ -297,9 +311,42 @@ export default function Step1NameAndDefine({
                 name="objectType"
                 render={({ field }) => (
                   <FormItem>
-                    <FormLabel>
-                      {t("wizard.step1.classificationType")}
-                    </FormLabel>
+                    <div className="flex items-center gap-1">
+                      <FormLabel className="text-primary-variant">
+                        {t("wizard.step1.classificationType")}
+                      </FormLabel>
+                      <Popover>
+                        <PopoverTrigger asChild>
+                          <Button
+                            variant="ghost"
+                            size="sm"
+                            className="h-4 w-4 p-0"
+                          >
+                            <LuInfo className="size-3" />
+                          </Button>
+                        </PopoverTrigger>
+                        <PopoverContent className="pointer-events-auto w-80 text-xs">
+                          <div className="flex flex-col gap-2">
+                            <div className="text-sm">
+                              {t("wizard.step1.classificationTypeDesc")}
+                            </div>
+                            <div className="mt-3 flex items-center text-primary">
+                              <a
+                                href={getLocaleDocUrl(
+                                  "configuration/custom_classification/object_classification#classification-type",
+                                )}
+                                target="_blank"
+                                rel="noopener noreferrer"
+                                className="inline cursor-pointer"
+                              >
+                                {t("readTheDocumentation", { ns: "common" })}
+                                <LuExternalLink className="ml-2 inline-flex size-3" />
+                              </a>
+                            </div>
+                          </div>
+                        </PopoverContent>
+                      </Popover>
+                    </div>
                     <FormControl>
                       <RadioGroup
                         onValueChange={field.onChange}
@@ -345,11 +392,50 @@ export default function Step1NameAndDefine({
 
           <div className="space-y-2">
             <div className="flex items-center justify-between">
-              <FormLabel>{t("wizard.step1.classes")}</FormLabel>
-              <MdAddBox
-                className="size-7 cursor-pointer text-primary hover:text-primary/80"
+              <div className="flex items-center gap-1">
+                <FormLabel className="text-primary-variant">
+                  {t("wizard.step1.classes")}
+                </FormLabel>
+                <Popover>
+                  <PopoverTrigger asChild>
+                    <Button variant="ghost" size="sm" className="h-4 w-4 p-0">
+                      <LuInfo className="size-3" />
+                    </Button>
+                  </PopoverTrigger>
+                  <PopoverContent className="pointer-events-auto w-80 text-xs">
+                    <div className="flex flex-col gap-2">
+                      <div className="text-sm">
+                        {watchedModelType === "state"
+                          ? t("wizard.step1.classesStateDesc")
+                          : t("wizard.step1.classesObjectDesc")}
+                      </div>
+                      <div className="mt-3 flex items-center text-primary">
+                        <a
+                          href={getLocaleDocUrl(
+                            watchedModelType === "state"
+                              ? "configuration/custom_classification/state_classification"
+                              : "configuration/custom_classification/object_classification",
+                          )}
+                          target="_blank"
+                          rel="noopener noreferrer"
+                          className="inline cursor-pointer"
+                        >
+                          {t("readTheDocumentation", { ns: "common" })}
+                          <LuExternalLink className="ml-2 inline-flex size-3" />
+                        </a>
+                      </div>
+                    </div>
+                  </PopoverContent>
+                </Popover>
+              </div>
+              <Button
+                type="button"
+                variant="secondary"
+                className="size-6 rounded-md bg-secondary-foreground p-1 text-background"
                 onClick={handleAddClass}
-              />
+              >
+                <LuPlus />
+              </Button>
             </div>
             <div className="space-y-2">
               {watchedClasses.map((_, index) => (

web/src/components/classification/wizard/Step2StateArea.tsx

@@ -8,8 +8,7 @@ import {
   PopoverContent,
   PopoverTrigger,
 } from "@/components/ui/popover";
-import { MdAddBox } from "react-icons/md";
-import { LuX } from "react-icons/lu";
+import { LuX, LuPlus } from "react-icons/lu";
 import { Stage, Layer, Rect, Transformer } from "react-konva";
 import Konva from "konva";
 import { useResizeObserver } from "@/hooks/resize-observer";
@@ -247,12 +246,11 @@ export default function Step2StateArea({
                 <PopoverTrigger asChild>
                   <Button
                     type="button"
-                    variant="ghost"
-                    size="icon"
-                    className="size-6 p-0"
+                    variant="secondary"
+                    className="size-6 rounded-md bg-secondary-foreground p-1 text-background"
                     aria-label="Add camera"
                   >
-                    <MdAddBox className="size-6 text-primary" />
+                    <LuPlus />
                   </Button>
                 </PopoverTrigger>
                 <PopoverContent
@@ -262,7 +260,7 @@ export default function Step2StateArea({
                   onOpenAutoFocus={(e) => e.preventDefault()}
                 >
                   <div className="flex flex-col gap-2">
-                    <Heading as="h4" className="text-sm font-medium">
+                    <Heading as="h4" className="text-sm text-primary-variant">
                       {t("wizard.step2.selectCamera")}
                     </Heading>
                     <div className="scrollbar-container flex max-h-[30vh] flex-col gap-1 overflow-y-auto">
@@ -285,7 +283,13 @@ export default function Step2StateArea({
                 </PopoverContent>
               </Popover>
             ) : (
-              <MdAddBox className="size-6 cursor-not-allowed text-muted" />
+              <Button
+                variant="secondary"
+                className="size-6 cursor-not-allowed rounded-md bg-muted p-1 text-muted-foreground"
+                disabled
+              >
+                <LuPlus />
+              </Button>
             )}
           </div>
 

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

@@ -1,11 +1,4 @@
 import { Button } from "@/components/ui/button";
-import {
-  Select,
-  SelectContent,
-  SelectItem,
-  SelectTrigger,
-  SelectValue,
-} from "@/components/ui/select";
 import { useTranslation } from "react-i18next";
 import { useState, useEffect, useCallback, useMemo } from "react";
 import ActivityIndicator from "@/components/indicators/activity-indicator";
@@ -47,6 +40,9 @@ export default function Step3ChooseExamples({
     [imageName: string]: string;
   }>(initialData?.imageClassifications || {});
   const [isTraining, setIsTraining] = useState(false);
+  const [isProcessing, setIsProcessing] = useState(false);
+  const [currentClassIndex, setCurrentClassIndex] = useState(0);
+  const [selectedImages, setSelectedImages] = useState<Set<string>>(new Set());
 
   const { data: trainImages, mutate: refreshTrainImages } = useSWR<string[]>(
     hasGenerated ? `classification/${step1Data.modelName}/train` : null,
@@ -57,16 +53,165 @@ export default function Step3ChooseExamples({
     return trainImages;
   }, [trainImages]);
 
-  const handleClassificationChange = useCallback(
-    (imageName: string, className: string) => {
-      setImageClassifications((prev) => ({
-        ...prev,
-        [imageName]: className,
-      }));
+  const toggleImageSelection = useCallback((imageName: string) => {
+    setSelectedImages((prev) => {
+      const newSet = new Set(prev);
+      if (newSet.has(imageName)) {
+        newSet.delete(imageName);
+      } else {
+        newSet.add(imageName);
+      }
+      return newSet;
+    });
+  }, []);
+
+  // Get all classes (excluding "none" - it will be auto-assigned)
+  const allClasses = useMemo(() => {
+    return [...step1Data.classes];
+  }, [step1Data.classes]);
+
+  const currentClass = allClasses[currentClassIndex];
+
+  const processClassificationsAndTrain = useCallback(
+    async (classifications: { [imageName: string]: string }) => {
+      // Step 1: Create config for the new model
+      const modelConfig: {
+        enabled: boolean;
+        name: string;
+        threshold: number;
+        state_config?: {
+          cameras: Record<string, { crop: number[] }>;
+          motion: boolean;
+        };
+        object_config?: { objects: string[]; classification_type: string };
+      } = {
+        enabled: true,
+        name: step1Data.modelName,
+        threshold: 0.8,
+      };
+
+      if (step1Data.modelType === "state") {
+        // State model config
+        const cameras: Record<string, { crop: number[] }> = {};
+        step2Data?.cameraAreas.forEach((area) => {
+          cameras[area.camera] = {
+            crop: area.crop,
+          };
+        });
+
+        modelConfig.state_config = {
+          cameras,
+          motion: true,
+        };
+      } else {
+        // Object model config
+        modelConfig.object_config = {
+          objects: step1Data.objectLabel ? [step1Data.objectLabel] : [],
+          classification_type: step1Data.objectType || "sub_label",
+        } as { objects: string[]; classification_type: string };
+      }
+
+      // Update config via config API
+      await axios.put("/config/set", {
+        requires_restart: 0,
+        update_topic: `config/classification/custom/${step1Data.modelName}`,
+        config_data: {
+          classification: {
+            custom: {
+              [step1Data.modelName]: modelConfig,
+            },
+          },
+        },
+      });
+
+      // Step 2: Classify each image by moving it to the correct category folder
+      const categorizePromises = Object.entries(classifications).map(
+        ([imageName, className]) => {
+          if (!className) return Promise.resolve();
+          return axios.post(
+            `/classification/${step1Data.modelName}/dataset/categorize`,
+            {
+              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"));
+      setIsTraining(true);
     },
-    [],
+    [step1Data, step2Data, t],
   );
 
+  const handleContinueClassification = useCallback(async () => {
+    // Mark selected images with current class
+    const newClassifications = { ...imageClassifications };
+    selectedImages.forEach((imageName) => {
+      newClassifications[imageName] = currentClass;
+    });
+
+    // Check if we're on the last class to select
+    const isLastClass = currentClassIndex === allClasses.length - 1;
+
+    if (isLastClass) {
+      // Assign remaining unclassified images
+      unknownImages.slice(0, 24).forEach((imageName) => {
+        if (!newClassifications[imageName]) {
+          // For state models with 2 classes, assign to the last class
+          // For object models, assign to "none"
+          if (step1Data.modelType === "state" && allClasses.length === 2) {
+            newClassifications[imageName] = allClasses[allClasses.length - 1];
+          } else {
+            newClassifications[imageName] = "none";
+          }
+        }
+      });
+
+      // All done, trigger training immediately
+      setImageClassifications(newClassifications);
+      setIsProcessing(true);
+
+      try {
+        await processClassificationsAndTrain(newClassifications);
+      } catch (error) {
+        const axiosError = error as {
+          response?: { data?: { message?: string; detail?: string } };
+          message?: string;
+        };
+        const errorMessage =
+          axiosError.response?.data?.message ||
+          axiosError.response?.data?.detail ||
+          axiosError.message ||
+          "Failed to classify images";
+
+        toast.error(
+          t("wizard.step3.errors.classifyFailed", { error: errorMessage }),
+        );
+        setIsProcessing(false);
+      }
+    } else {
+      // Move to next class
+      setImageClassifications(newClassifications);
+      setCurrentClassIndex((prev) => prev + 1);
+      setSelectedImages(new Set());
+    }
+  }, [
+    selectedImages,
+    currentClass,
+    currentClassIndex,
+    allClasses,
+    imageClassifications,
+    unknownImages,
+    step1Data,
+    processClassificationsAndTrain,
+    t,
+  ]);
+
   const generateExamples = useCallback(async () => {
     setIsGenerating(true);
 
@@ -135,77 +280,9 @@ export default function Step3ChooseExamples({
   }, []);
 
   const handleContinue = useCallback(async () => {
+    setIsProcessing(true);
     try {
-      // Step 1: Create config for the new model
-      const modelConfig: {
-        enabled: boolean;
-        name: string;
-        threshold: number;
-        state_config?: {
-          cameras: Record<string, { crop: number[] }>;
-          motion: boolean;
-        };
-        object_config?: { objects: string[]; classification_type: string };
-      } = {
-        enabled: true,
-        name: step1Data.modelName,
-        threshold: 0.8,
-      };
-
-      if (step1Data.modelType === "state") {
-        // State model config
-        const cameras: Record<string, { crop: number[] }> = {};
-        step2Data?.cameraAreas.forEach((area) => {
-          cameras[area.camera] = {
-            crop: area.crop,
-          };
-        });
-
-        modelConfig.state_config = {
-          cameras,
-          motion: true,
-        };
-      } else {
-        // Object model config
-        modelConfig.object_config = {
-          objects: step1Data.objectLabel ? [step1Data.objectLabel] : [],
-          classification_type: step1Data.objectType || "sub_label",
-        } as { objects: string[]; classification_type: string };
-      }
-
-      // Update config via config API
-      await axios.put("/config/set", {
-        requires_restart: 0,
-        update_topic: `config/classification/custom/${step1Data.modelName}`,
-        config_data: {
-          classification: {
-            custom: {
-              [step1Data.modelName]: modelConfig,
-            },
-          },
-        },
-      });
-
-      // Step 2: Classify each image by moving it to the correct category folder
-      const categorizePromises = Object.entries(imageClassifications).map(
-        ([imageName, className]) => {
-          if (!className) return Promise.resolve();
-          return axios.post(
-            `/classification/${step1Data.modelName}/dataset/categorize`,
-            {
-              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"));
-      setIsTraining(true);
+      await processClassificationsAndTrain(imageClassifications);
     } catch (error) {
       const axiosError = error as {
         response?: { data?: { message?: string; detail?: string } };
@@ -220,14 +297,25 @@ export default function Step3ChooseExamples({
       toast.error(
         t("wizard.step3.errors.classifyFailed", { error: errorMessage }),
       );
+      setIsProcessing(false);
     }
-  }, [imageClassifications, step1Data, step2Data, t]);
+  }, [imageClassifications, processClassificationsAndTrain, t]);
+
+  const unclassifiedImages = useMemo(() => {
+    if (!unknownImages) return [];
+    const images = unknownImages.slice(0, 24);
+
+    // Only filter if we have any classifications
+    if (Object.keys(imageClassifications).length === 0) {
+      return images;
+    }
+
+    return images.filter((img) => !imageClassifications[img]);
+  }, [unknownImages, imageClassifications]);
 
   const allImagesClassified = useMemo(() => {
-    if (!unknownImages || unknownImages.length === 0) return false;
-    const imagesToClassify = unknownImages.slice(0, 24);
-    return imagesToClassify.every((img) => imageClassifications[img]);
-  }, [unknownImages, imageClassifications]);
+    return unclassifiedImages.length === 0;
+  }, [unclassifiedImages]);
 
   return (
     <div className="flex flex-col gap-6">
@@ -260,9 +348,18 @@ export default function Step3ChooseExamples({
         </div>
       ) : hasGenerated ? (
         <div className="flex flex-col gap-4">
-          <div className="text-sm text-muted-foreground">
-            {t("wizard.step3.description")}
-          </div>
+          {!allImagesClassified && (
+            <div className="text-center">
+              <h3 className="text-lg font-medium">
+                {t("wizard.step3.selectImagesPrompt", {
+                  className: currentClass,
+                })}
+              </h3>
+              <p className="text-sm text-muted-foreground">
+                {t("wizard.step3.selectImagesDescription")}
+              </p>
+            </div>
+          )}
           <div
             className={cn(
               "rounded-lg bg-secondary/30 p-4",
@@ -270,58 +367,42 @@ export default function Step3ChooseExamples({
             )}
           >
             {!unknownImages || unknownImages.length === 0 ? (
-              <div className="flex h-[40vh] items-center justify-center">
+              <div className="flex h-[40vh] flex-col items-center justify-center gap-4">
                 <p className="text-muted-foreground">
                   {t("wizard.step3.noImages")}
                 </p>
+                <Button onClick={generateExamples} variant="select">
+                  {t("wizard.step3.retryGenerate")}
+                </Button>
+              </div>
+            ) : allImagesClassified && isProcessing ? (
+              <div className="flex h-[40vh] flex-col items-center justify-center gap-4">
+                <ActivityIndicator className="size-12" />
+                <p className="text-lg font-medium">
+                  {t("wizard.step3.classifying")}
+                </p>
               </div>
             ) : (
-              <div className="grid grid-cols-2 gap-3 sm:grid-cols-6">
-                {unknownImages.slice(0, 24).map((imageName, index) => (
-                  <div
-                    key={imageName}
-                    className="group relative aspect-square overflow-hidden rounded-lg border bg-background"
-                  >
-                    <img
-                      src={`${baseUrl}clips/${step1Data.modelName}/train/${imageName}`}
-                      alt={`Example ${index + 1}`}
-                      className="h-full w-full object-cover"
-                    />
-                    <div className="absolute bottom-0 left-0 right-0 p-2">
-                      <Select
-                        value={imageClassifications[imageName] || ""}
-                        onValueChange={(value) =>
-                          handleClassificationChange(imageName, value)
-                        }
-                      >
-                        <SelectTrigger className="h-7 bg-background/20 text-xs">
-                          <SelectValue
-                            placeholder={t("wizard.step3.selectClass")}
-                          />
-                        </SelectTrigger>
-                        <SelectContent>
-                          {step1Data.modelType === "object" && (
-                            <SelectItem
-                              value="none"
-                              className="cursor-pointer text-xs"
-                            >
-                              {t("wizard.step3.none")}
-                            </SelectItem>
-                          )}
-                          {step1Data.classes.map((className) => (
-                            <SelectItem
-                              key={className}
-                              value={className}
-                              className="cursor-pointer text-xs"
-                            >
-                              {className}
-                            </SelectItem>
-                          ))}
-                        </SelectContent>
-                      </Select>
+              <div className="grid grid-cols-2 gap-4 sm:grid-cols-6">
+                {unclassifiedImages.map((imageName, index) => {
+                  const isSelected = selectedImages.has(imageName);
+                  return (
+                    <div
+                      key={imageName}
+                      className={cn(
+                        "aspect-square cursor-pointer overflow-hidden rounded-lg border-2 bg-background transition-all",
+                        isSelected && "border-selected ring-2 ring-selected",
+                      )}
+                      onClick={() => toggleImageSelection(imageName)}
+                    >
+                      <img
+                        src={`${baseUrl}clips/${step1Data.modelName}/train/${imageName}`}
+                        alt={`Example ${index + 1}`}
+                        className="h-full w-full object-cover"
+                      />
                     </div>
-                  </div>
-                ))}
+                  );
+                })}
               </div>
             )}
           </div>
@@ -344,11 +425,16 @@ export default function Step3ChooseExamples({
           </Button>
           <Button
             type="button"
-            onClick={handleContinue}
+            onClick={
+              allImagesClassified
+                ? handleContinue
+                : handleContinueClassification
+            }
             variant="select"
             className="flex items-center justify-center gap-2 sm:flex-1"
-            disabled={!hasGenerated || isGenerating || !allImagesClassified}
+            disabled={!hasGenerated || isGenerating || isProcessing}
           >
+            {isProcessing && <ActivityIndicator className="size-4" />}
             {t("button.continue", { ns: "common" })}
           </Button>
         </div>

web/src/views/classification/ModelSelectionView.tsx

@@ -10,13 +10,14 @@ import {
   CustomClassificationModelConfig,
   FrigateConfig,
 } from "@/types/frigateConfig";
-import { useMemo, useState } from "react";
+import { useEffect, useMemo, useState } from "react";
 import { isMobile } from "react-device-detect";
 import { useTranslation } from "react-i18next";
 import { FaFolderPlus } from "react-icons/fa";
 import { MdModelTraining } from "react-icons/md";
 import useSWR from "swr";
 import Heading from "@/components/ui/heading";
+import { useOverlayState } from "@/hooks/use-overlay-state";
 
 const allModelTypes = ["objects", "states"] as const;
 type ModelType = (typeof allModelTypes)[number];
@@ -28,8 +29,12 @@ export default function ModelSelectionView({
   onClick,
 }: ModelSelectionViewProps) {
   const { t } = useTranslation(["views/classificationModel"]);
-  const [page, setPage] = useState<ModelType>("objects");
-  const [pageToggle, setPageToggle] = useOptimisticState(page, setPage, 100);
+  const [page, setPage] = useOverlayState<ModelType>("objects", "objects");
+  const [pageToggle, setPageToggle] = useOptimisticState(
+    page || "objects",
+    setPage,
+    100,
+  );
   const { data: config, mutate: refreshConfig } = useSWR<FrigateConfig>(
     "config",
     {
@@ -37,6 +42,12 @@ export default function ModelSelectionView({
     },
   );
 
+  // title
+
+  useEffect(() => {
+    document.title = t("documentTitle");
+  }, [t]);
+
   // data
 
   const classificationConfigs = useMemo(() => {
@@ -69,25 +80,11 @@ export default function ModelSelectionView({
     return <ActivityIndicator />;
   }
 
-  if (classificationConfigs.length == 0) {
-    return (
-      <>
-        <ClassificationModelWizardDialog
-          open={newModel}
-          onClose={() => {
-            setNewModel(false);
-            refreshConfig();
-          }}
-        />
-        <NoModelsView onCreateModel={() => setNewModel(true)} />;
-      </>
-    );
-  }
-
   return (
     <div className="flex size-full flex-col p-2">
       <ClassificationModelWizardDialog
         open={newModel}
+        defaultModelType={pageToggle === "objects" ? "object" : "state"}
         onClose={() => {
           setNewModel(false);
           refreshConfig();
@@ -103,7 +100,6 @@ export default function ModelSelectionView({
             value={pageToggle}
             onValueChange={(value: ModelType) => {
               if (value) {
-                // Restrict viewer navigation
                 setPageToggle(value);
               }
             }}
@@ -136,31 +132,45 @@ export default function ModelSelectionView({
         </div>
       </div>
       <div className="flex size-full gap-2 p-2">
-        {selectedClassificationConfigs.map((config) => (
-          <ModelCard
-            key={config.name}
-            config={config}
-            onClick={() => onClick(config)}
+        {selectedClassificationConfigs.length === 0 ? (
+          <NoModelsView
+            onCreateModel={() => setNewModel(true)}
+            modelType={pageToggle}
           />
-        ))}
+        ) : (
+          selectedClassificationConfigs.map((config) => (
+            <ModelCard
+              key={config.name}
+              config={config}
+              onClick={() => onClick(config)}
+            />
+          ))
+        )}
       </div>
     </div>
   );
 }
 
-function NoModelsView({ onCreateModel }: { onCreateModel: () => void }) {
+function NoModelsView({
+  onCreateModel,
+  modelType,
+}: {
+  onCreateModel: () => void;
+  modelType: ModelType;
+}) {
   const { t } = useTranslation(["views/classificationModel"]);
+  const typeKey = modelType === "objects" ? "object" : "state";
 
   return (
     <div className="flex size-full items-center justify-center">
       <div className="flex flex-col items-center gap-2">
         <MdModelTraining className="size-8" />
-        <Heading as="h4">{t("noModels.title")}</Heading>
+        <Heading as="h4">{t(`noModels.${typeKey}.title`)}</Heading>
         <div className="mb-3 text-center text-secondary-foreground">
-          {t("noModels.description")}
+          {t(`noModels.${typeKey}.description`)}
         </div>
         <Button size="sm" variant="select" onClick={onCreateModel}>
-          {t("noModels.buttonText")}
+          {t(`noModels.${typeKey}.buttonText`)}
         </Button>
       </div>
     </div>

web/src/views/classification/ModelTrainingView.tsx

@@ -642,6 +642,7 @@ function DatasetGrid({
               filepath: `clips/${modelName}/dataset/${categoryName}/${image}`,
               name: "",
             }}
+            showArea={false}
             selected={selectedImages.includes(image)}
             i18nLibrary="views/classificationModel"
             onClick={(data, _) => onClickImages([data.filename], true)}