frigate/api/classification.py

@@ -595,13 +595,9 @@ def get_classification_dataset(name: str):
             "last_training_image_count": 0,
             "current_image_count": current_image_count,
             "new_images_count": current_image_count,
-            "dataset_changed": current_image_count > 0,
         }
     else:
         last_training_count = metadata.get("last_training_image_count", 0)
-        # Dataset has changed if count is different (either added or deleted images)
-        dataset_changed = current_image_count != last_training_count
-        # Only show positive count for new images (ignore deletions in the count display)
         new_images_count = max(0, current_image_count - last_training_count)
         training_metadata = {
             "has_trained": True,
@@ -609,7 +605,6 @@ def get_classification_dataset(name: str):
             "last_training_image_count": last_training_count,
             "current_image_count": current_image_count,
             "new_images_count": new_images_count,
-            "dataset_changed": dataset_changed,
         }
 
     return JSONResponse(
@@ -953,29 +948,31 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
     dependencies=[Depends(require_role(["admin"]))],
     summary="Delete a classification model",
     description="""Deletes a specific classification model and all its associated data.
-    Works even if the model is not in the config (e.g., partially created during wizard).
-    Returns a success message.""",
+    The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
 )
 def delete_classification_model(request: Request, name: str):
-    sanitized_name = sanitize_filename(name)
+    config: FrigateConfig = request.app.frigate_config
+
+    if name not in config.classification.custom:
+        return JSONResponse(
+            content=(
+                {
+                    "success": False,
+                    "message": f"{name} is not a known classification model.",
+                }
+            ),
+            status_code=404,
+        )
 
     # Delete the classification model's data directory in clips
-    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
+    data_dir = os.path.join(CLIPS_DIR, sanitize_filename(name))
     if os.path.exists(data_dir):
-        try:
         shutil.rmtree(data_dir)
-            logger.info(f"Deleted classification data directory for {name}")
-        except Exception as e:
-            logger.debug(f"Failed to delete data directory for {name}: {e}")
 
     # Delete the classification model's files in model_cache
-    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
+    model_dir = os.path.join(MODEL_CACHE_DIR, sanitize_filename(name))
     if os.path.exists(model_dir):
-        try:
         shutil.rmtree(model_dir)
-            logger.info(f"Deleted classification model directory for {name}")
-        except Exception as e:
-            logger.debug(f"Failed to delete model directory for {name}: {e}")
 
     return JSONResponse(
         content=(

frigate/data_processing/common/audio_transcription/model.py

@@ -4,6 +4,7 @@ import logging
 import os
 
 import sherpa_onnx
+from faster_whisper.utils import download_model
 
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.const import MODEL_CACHE_DIR
@@ -24,9 +25,6 @@ class AudioTranscriptionModelRunner:
 
         if model_size == "large":
             # use the Whisper download function instead of our own
-            # Import dynamically to avoid crashes on systems without AVX support
-            from faster_whisper.utils import download_model
-
             logger.debug("Downloading Whisper audio transcription model")
             download_model(
                 size_or_id="small" if device == "cuda" else "tiny",

frigate/data_processing/post/audio_transcription.py

@@ -6,6 +6,7 @@ import threading
 import time
 from typing import Optional
 
+from faster_whisper import WhisperModel
 from peewee import DoesNotExist
 
 from frigate.comms.inter_process import InterProcessRequestor
@@ -50,9 +51,6 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
 
     def __build_recognizer(self) -> None:
         try:
-            # Import dynamically to avoid crashes on systems without AVX support
-            from faster_whisper import WhisperModel
-
             self.recognizer = WhisperModel(
                 model_size_or_path="small",
                 device="cuda"

frigate/detectors/detection_runners.py

@@ -394,11 +394,7 @@ class OpenVINOModelRunner(BaseModelRunner):
                     self.infer_request.set_input_tensor(input_index, input_tensor)
 
                 # Run inference
-                try:
                 self.infer_request.infer()
-                except Exception as e:
-                    logger.error(f"Error during OpenVINO inference: {e}")
-                    return []
 
             # Get all output tensors
             outputs = []

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

@@ -16,7 +16,6 @@
   "tooltip": {
     "trainingInProgress": "Model is currently training",
     "noNewImages": "No new images to train. Classify more images in the dataset first.",
-    "noChanges": "No changes to the dataset since last training.",
     "modelNotReady": "Model is not ready for training"
   },
   "toast": {
@@ -44,9 +43,7 @@
   },
   "deleteCategory": {
     "title": "Delete Class",
-    "desc": "Are you sure you want to delete the class {{name}}? This will permanently delete all associated images and require re-training the model.",
-    "minClassesTitle": "Cannot Delete Class",
-    "minClassesDesc": "A classification model must have at least 2 classes. Add another class before deleting this one."
+    "desc": "Are you sure you want to delete the class {{name}}? This will permanently delete all associated images and require re-training the model."
   },
   "deleteModel": {
     "title": "Delete Classification Model",

web/src/components/classification/ClassificationModelWizardDialog.tsx

@@ -15,7 +15,6 @@ import Step3ChooseExamples, {
 } from "./wizard/Step3ChooseExamples";
 import { cn } from "@/lib/utils";
 import { isDesktop } from "react-device-detect";
-import axios from "axios";
 
 const OBJECT_STEPS = [
   "wizard.steps.nameAndDefine",
@@ -121,18 +120,7 @@ export default function ClassificationModelWizardDialog({
     dispatch({ type: "PREVIOUS_STEP" });
   };
 
-  const handleCancel = async () => {
-    // Clean up any generated training images if we're cancelling from Step 3
-    if (wizardState.step1Data && wizardState.step3Data?.examplesGenerated) {
-      try {
-        await axios.delete(
-          `/classification/${wizardState.step1Data.modelName}`,
-        );
-      } catch (error) {
-        // Silently fail - user is already cancelling
-      }
-    }
-
+  const handleCancel = () => {
     dispatch({ type: "RESET" });
     onClose();
   };

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

@@ -165,15 +165,18 @@ export default function Step3ChooseExamples({
     const isLastClass = currentClassIndex === allClasses.length - 1;
 
     if (isLastClass) {
-      // For object models, assign remaining unclassified images to "none"
-      // For state models, this should never happen since we require all images to be classified
-      if (step1Data.modelType !== "state") {
+      // 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);
@@ -313,15 +316,8 @@ export default function Step3ChooseExamples({
       return images;
     }
 
-    // If we're viewing a previous class (going back), show images for that class
-    // Otherwise show only unclassified images
-    const currentClassInView = allClasses[currentClassIndex];
-    return images.filter((img) => {
-      const imgClass = imageClassifications[img];
-      // Show if: unclassified OR classified with current class we're viewing
-      return !imgClass || imgClass === currentClassInView;
-    });
-  }, [unknownImages, imageClassifications, allClasses, currentClassIndex]);
+    return images.filter((img) => !imageClassifications[img]);
+  }, [unknownImages, imageClassifications]);
 
   const allImagesClassified = useMemo(() => {
     return unclassifiedImages.length === 0;
@@ -330,26 +326,15 @@ export default function Step3ChooseExamples({
   // For state models on the last class, require all images to be classified
   const isLastClass = currentClassIndex === allClasses.length - 1;
   const canProceed = useMemo(() => {
-    if (step1Data.modelType === "state" && isLastClass) {
-      // Check if all 24 images will be classified after current selections are applied
-      const totalImages = unknownImages.slice(0, 24).length;
-
-      // Count images that will be classified (either already classified or currently selected)
-      const allImages = unknownImages.slice(0, 24);
-      const willBeClassified = allImages.filter((img) => {
-        return imageClassifications[img] || selectedImages.has(img);
-      }).length;
-
-      return willBeClassified >= totalImages;
+    if (
+      step1Data.modelType === "state" &&
+      isLastClass &&
+      !allImagesClassified
+    ) {
+      return false;
     }
     return true;
-  }, [
-    step1Data.modelType,
-    isLastClass,
-    unknownImages,
-    imageClassifications,
-    selectedImages,
-  ]);
+  }, [step1Data.modelType, isLastClass, allImagesClassified]);
 
   const handleBack = useCallback(() => {
     if (currentClassIndex > 0) {

web/src/components/overlay/ImageShadowOverlay.tsx

@@ -12,13 +12,13 @@ export function ImageShadowOverlay({
     <>
       <div
         className={cn(
-          "pointer-events-none absolute inset-x-0 top-0 z-10 h-[30%] w-full rounded-lg bg-gradient-to-b from-black/20 to-transparent",
+          "pointer-events-none absolute inset-x-0 top-0 z-10 h-[30%] w-full rounded-lg bg-gradient-to-b from-black/20 to-transparent md:rounded-2xl",
           upperClassName,
         )}
       />
       <div
         className={cn(
-          "pointer-events-none absolute inset-x-0 bottom-0 z-10 h-[10%] w-full rounded-lg bg-gradient-to-t from-black/20 to-transparent",
+          "pointer-events-none absolute inset-x-0 bottom-0 z-10 h-[10%] w-full rounded-lg bg-gradient-to-t from-black/20 to-transparent md:rounded-2xl",
           lowerClassName,
         )}
       />

web/src/components/player/BirdseyeLivePlayer.tsx

@@ -77,10 +77,7 @@ export default function BirdseyeLivePlayer({
       )}
       onClick={onClick}
     >
-      <ImageShadowOverlay
-        upperClassName="md:rounded-2xl"
-        lowerClassName="md:rounded-2xl"
-      />
+      <ImageShadowOverlay />
       <div className="size-full" ref={playerRef}>
         {player}
       </div>

web/src/components/player/LivePlayer.tsx

@@ -331,10 +331,7 @@ export default function LivePlayer({
     >
       {cameraEnabled &&
         ((showStillWithoutActivity && !liveReady) || liveReady) && (
-          <ImageShadowOverlay
-            upperClassName="md:rounded-2xl"
-            lowerClassName="md:rounded-2xl"
-          />
+          <ImageShadowOverlay />
         )}
       {player}
       {cameraEnabled &&

web/src/context/detail-stream-context.tsx

@@ -1,10 +1,4 @@
-import React, {
-  createContext,
-  useContext,
-  useState,
-  useEffect,
-  useRef,
-} from "react";
+import React, { createContext, useContext, useState, useEffect } from "react";
 import { FrigateConfig } from "@/types/frigateConfig";
 import useSWR from "swr";
 
@@ -42,23 +36,6 @@ export function DetailStreamProvider({
     () => initialSelectedObjectIds ?? [],
   );
 
-  // When the parent provides a new initialSelectedObjectIds (for example
-  // when navigating between search results) update the selection so children
-  // like `ObjectTrackOverlay` receive the new ids immediately. We only
-  // perform this update when the incoming value actually changes.
-  useEffect(() => {
-    if (
-      initialSelectedObjectIds &&
-      (initialSelectedObjectIds.length !== selectedObjectIds.length ||
-        initialSelectedObjectIds.some((v, i) => selectedObjectIds[i] !== v))
-    ) {
-      setSelectedObjectIds(initialSelectedObjectIds);
-    }
-    // Intentionally include selectedObjectIds to compare previous value and
-    // avoid overwriting user interactions unless the incoming prop changed.
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [initialSelectedObjectIds]);
-
   const toggleObjectSelection = (id: string | undefined) => {
     if (id === undefined) {
       setSelectedObjectIds([]);
@@ -86,33 +63,10 @@ export function DetailStreamProvider({
     setAnnotationOffset(cfgOffset);
   }, [config, camera]);
 
-  // Clear selected objects when exiting detail mode or when the camera
-  // changes for providers that are not initialized with an explicit
-  // `initialSelectedObjectIds` (e.g., the RecordingView). For providers
-  // that receive `initialSelectedObjectIds` (like SearchDetailDialog) we
-  // avoid clearing on camera change to prevent a race with children that
-  // immediately set selection when mounting.
-  const prevCameraRef = useRef<string | undefined>(undefined);
+  // Clear selected objects when exiting detail mode or changing cameras
   useEffect(() => {
-    // Always clear when leaving detail mode
-    if (!isDetailMode) {
     setSelectedObjectIds([]);
-      prevCameraRef.current = camera;
-      return;
-    }
-
-    // If camera changed and the parent did not provide initialSelectedObjectIds,
-    // clear selection to preserve previous behavior.
-    if (
-      prevCameraRef.current !== undefined &&
-      prevCameraRef.current !== camera &&
-      initialSelectedObjectIds === undefined
-    ) {
-      setSelectedObjectIds([]);
-    }
-
-    prevCameraRef.current = camera;
-  }, [isDetailMode, camera, initialSelectedObjectIds]);
+  }, [isDetailMode, camera]);
 
   const value: DetailStreamContextType = {
     selectedObjectIds,

web/src/views/classification/ModelTrainingView.tsx

@@ -126,7 +126,6 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
       last_training_image_count: number;
       current_image_count: number;
       new_images_count: number;
-      dataset_changed: boolean;
     } | null;
   }>(`classification/${model.name}/dataset`);
 
@@ -265,11 +264,10 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
               );
             }
 
-            // Always refresh dataset to update the categories list
-            refreshDataset();
-
             if (pageToggle == "train") {
               refreshTrain();
+            } else {
+              refreshDataset();
             }
           }
         })
@@ -447,7 +445,7 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
                   variant={modelState == "failed" ? "destructive" : "select"}
                   disabled={
                     (modelState != "complete" && modelState != "failed") ||
-                    !trainingMetadata?.dataset_changed
+                    (trainingMetadata?.new_images_count ?? 0) === 0
                   }
                 >
                   {modelState == "training" ? (
@@ -468,14 +466,14 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
                   )}
                 </Button>
               </TooltipTrigger>
-              {(!trainingMetadata?.dataset_changed ||
+              {((trainingMetadata?.new_images_count ?? 0) === 0 ||
                 (modelState != "complete" && modelState != "failed")) && (
                 <TooltipPortal>
                   <TooltipContent>
                     {modelState == "training"
                       ? t("tooltip.trainingInProgress")
-                      : !trainingMetadata?.dataset_changed
-                        ? t("tooltip.noChanges")
+                      : trainingMetadata?.new_images_count === 0
+                        ? t("tooltip.noNewImages")
                         : t("tooltip.modelNotReady")}
                   </TooltipContent>
                 </TooltipPortal>
@@ -573,28 +571,13 @@ function LibrarySelector({
       >
         <DialogContent>
           <DialogHeader>
-            <DialogTitle>
-              {Object.keys(dataset).length <= 2
-                ? t("deleteCategory.minClassesTitle")
-                : t("deleteCategory.title")}
-            </DialogTitle>
+            <DialogTitle>{t("deleteCategory.title")}</DialogTitle>
             <DialogDescription>
-              {Object.keys(dataset).length <= 2
-                ? t("deleteCategory.minClassesDesc")
-                : t("deleteCategory.desc", { name: confirmDelete })}
+              {t("deleteCategory.desc", { name: confirmDelete })}
             </DialogDescription>
           </DialogHeader>
           <div className="flex justify-end gap-2">
-            {Object.keys(dataset).length <= 2 ? (
             <Button variant="outline" onClick={() => setConfirmDelete(null)}>
-                {t("button.ok", { ns: "common" })}
-              </Button>
-            ) : (
-              <>
-                <Button
-                  variant="outline"
-                  onClick={() => setConfirmDelete(null)}
-                >
               {t("button.cancel", { ns: "common" })}
             </Button>
             <Button
@@ -609,8 +592,6 @@ function LibrarySelector({
             >
               {t("button.delete", { ns: "common" })}
             </Button>
-              </>
-            )}
           </div>
         </DialogContent>
       </Dialog>