docs/docs/configuration/custom_classification/object_classification.md

@@ -95,8 +95,6 @@ The system will automatically generate example images from detected objects matc
 
 When choosing which objects to classify, start with a small number of visually distinct classes and ensure your training samples match camera viewpoints and distances typical for those objects.
 
-If examples for some of your classes do not appear in the grid, you can continue configuring the model without them. New images will begin to appear in the Recent Classifications view. When your missing classes are seen, classify them from this view and retrain your model.
-
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.

docs/docs/troubleshooting/memory.md

@@ -9,20 +9,8 @@ Frigate includes built-in memory profiling using [memray](https://bloomberg.gith
 
 Memory profiling is controlled via the `FRIGATE_MEMRAY_MODULES` environment variable. Set it to a comma-separated list of module names you want to profile:
 
-```yaml
-# docker-compose example
-services:
-  frigate:
-    ...
-    environment:
-      - FRIGATE_MEMRAY_MODULES=frigate.embeddings,frigate.capture
-```
-
 ```bash
-# docker run example
-docker run -e FRIGATE_MEMRAY_MODULES="frigate.embeddings" \
-   ...
-   --name frigate <frigate_image>
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
 ```
 
 ### Module Names
@@ -36,12 +24,11 @@ Frigate processes are named using a module-based naming scheme. Common module na
 - `frigate.output` - Output processing
 - `frigate.audio_manager` - Audio processing
 - `frigate.embeddings` - Embeddings processing
-- `frigate.embeddings_manager` - Embeddings manager
 
 You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
 
 ```bash
-FRIGATE_MEMRAY_MODULES=frigate.capture:front_door
+export FRIGATE_MEMRAY_MODULES="frigate.capture:front_door"
 ```
 
 When you specify a module name (e.g., `frigate.capture`), all processes with that module prefix will be profiled. For example, `frigate.capture` will profile all camera capture processes.
@@ -68,20 +55,11 @@ After a process exits normally, you'll find HTML reports in `/config/memray_repo
 
 If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
 
-- Run `memray` inside the Frigate container:
-
 ```bash
-docker-compose exec frigate memray flamegraph /config/memray_reports/<module_name>.bin
-# or
-docker exec -it <container_name_or_id> memray flamegraph /config/memray_reports/<module_name>.bin
+memray flamegraph /config/memray_reports/<module_name>.bin
 ```
 
-- You can also copy the `.bin` file to the host and run `memray` locally if you have it installed:
-
-```bash
-docker cp <container_name_or_id>:/config/memray_reports/<module_name>.bin /tmp/
-memray flamegraph /tmp/<module_name>.bin
-```
+This will generate an HTML file that you can open in your browser.
 
 ## Understanding the Reports
 
@@ -132,4 +110,20 @@ The interactive HTML reports allow you to:
 - Check that memray is properly installed (included by default in Frigate)
 - Verify the process actually started and ran (check process logs)
 
+## Example Usage
+
+```bash
+# Enable profiling for review and capture modules
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
+
+# Start Frigate
+# ... let it run for a while ...
+
+# Check for reports
+ls -lh /config/memray_reports/
+
+# If a process crashed, manually generate report
+memray flamegraph /config/memray_reports/frigate_capture_front_door.bin
+```
+
 For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).

frigate/api/classification.py

@@ -40,7 +40,6 @@ from frigate.util.classification import (
     collect_state_classification_examples,
     get_dataset_image_count,
     read_training_metadata,
-    write_training_metadata,
 )
 from frigate.util.file import get_event_snapshot
 
@@ -843,12 +842,6 @@ def rename_classification_category(
 
     try:
         os.rename(old_folder, new_folder)
-
-        # Mark dataset as ready to train by resetting training metadata
-        # This ensures the dataset is marked as changed after renaming
-        sanitized_name = sanitize_filename(name)
-        write_training_metadata(sanitized_name, 0)
-
         return JSONResponse(
             content=(
                 {

frigate/data_processing/common/license_plate/mixin.py

@@ -374,9 +374,6 @@ class LicensePlateProcessingMixin:
                             combined_plate = re.sub(
                                 pattern, replacement, combined_plate
                             )
-                            logger.debug(
-                                f"{camera}: Processing replace rule: '{pattern}' -> '{replacement}', result: '{combined_plate}'"
-                            )
                     except re.error as e:
                         logger.warning(
                             f"{camera}: Invalid regex in replace_rules '{pattern}': {e}"
@@ -384,7 +381,7 @@ class LicensePlateProcessingMixin:
 
             if combined_plate != original_combined:
                 logger.debug(
-                    f"{camera}: All rules applied: '{original_combined}' -> '{combined_plate}'"
+                    f"{camera}: Rules applied: '{original_combined}' -> '{combined_plate}'"
                 )
 
             # Compute the combined area for qualifying boxes

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

@@ -45,12 +45,6 @@ export default function Step3ChooseExamples({
   const [isProcessing, setIsProcessing] = useState(false);
   const [currentClassIndex, setCurrentClassIndex] = useState(0);
   const [selectedImages, setSelectedImages] = useState<Set<string>>(new Set());
-  const [cacheKey, setCacheKey] = useState<number>(Date.now());
-  const [loadedImages, setLoadedImages] = useState<Set<string>>(new Set());
-
-  const handleImageLoad = useCallback((imageName: string) => {
-    setLoadedImages((prev) => new Set(prev).add(imageName));
-  }, []);
 
   const { data: trainImages, mutate: refreshTrainImages } = useSWR<string[]>(
     hasGenerated ? `classification/${step1Data.modelName}/train` : null,
@@ -338,8 +332,6 @@ export default function Step3ChooseExamples({
       setHasGenerated(true);
       toast.success(t("wizard.step3.generateSuccess"));
 
-      // Update cache key to force image reload
-      setCacheKey(Date.now());
       await refreshTrainImages();
     } catch (error) {
       const axiosError = error as {
@@ -573,16 +565,10 @@ export default function Step3ChooseExamples({
                       )}
                       onClick={() => toggleImageSelection(imageName)}
                     >
-                      {!loadedImages.has(imageName) && (
-                        <div className="flex h-full items-center justify-center">
-                          <ActivityIndicator className="size-6" />
-                        </div>
-                      )}
                       <img
-                        src={`${baseUrl}clips/${step1Data.modelName}/train/${imageName}?t=${cacheKey}`}
+                        src={`${baseUrl}clips/${step1Data.modelName}/train/${imageName}`}
                         alt={`Example ${index + 1}`}
                         className="h-full w-full object-cover"
-                        onLoad={() => handleImageLoad(imageName)}
                       />
                     </div>
                   );