updated negative feedback

backend/ml/train_detector_model.py

@@ -37,7 +37,8 @@ def count_yolo_samples(dataset_root: Path, split: str) -> int:
             continue
 
         label_path = labels_dir / f"{image_path.stem}.txt"
-        if label_path.exists() and label_path.stat().st_size > 0:
+        # Eine vorhandene, leere Labeldatei ist ein gültiges YOLO-Negativbeispiel.
+        if label_path.exists() and label_path.is_file():
             count += 1
 
     return count

backend/training.go

@@ -99,6 +99,7 @@ type trainingUncertainCandidate struct {
 type TrainingFeedbackRequest struct {
 	SampleID   string              `json:"sampleId"`
 	Accepted   bool                `json:"accepted"`
+	Negative   bool                `json:"negative,omitempty"`
 	Correction *TrainingCorrection `json:"correction,omitempty"`
 	Notes      string              `json:"notes,omitempty"`
 }
@@ -107,6 +108,7 @@ type TrainingFeedbackUpdateRequest struct {
 	SampleID   string              `json:"sampleId"`
 	AnsweredAt string              `json:"answeredAt"`
 	Accepted   bool                `json:"accepted"`
+	Negative   bool                `json:"negative,omitempty"`
 	Correction *TrainingCorrection `json:"correction,omitempty"`
 	Notes      string              `json:"notes,omitempty"`
 }
@@ -126,6 +128,7 @@ type TrainingAnnotation struct {
 	AnsweredAt      string              `json:"answeredAt"`
 	Prediction      TrainingPrediction  `json:"prediction"`
 	Accepted        bool                `json:"accepted"`
+	Negative        bool                `json:"negative,omitempty"`
 	Correction      *TrainingCorrection `json:"correction,omitempty"`
 	Notes           string              `json:"notes,omitempty"`
 }
@@ -176,6 +179,7 @@ type TrainingStatsResponse struct {
 	FeedbackCount  int                 `json:"feedbackCount"`
 	AcceptedCount  int                 `json:"acceptedCount"`
 	CorrectedCount int                 `json:"correctedCount"`
+	NegativeCount  int                 `json:"negativeCount"`
 	SampleCount    int                 `json:"sampleCount"`
 	BoxCount       int                 `json:"boxCount"`
 	ModelAvailable bool                `json:"modelAvailable"`
@@ -333,12 +337,17 @@ func trainingFilterAnnotations(
 	for _, item := range items {
 		switch cleanFilter {
 		case "accepted":
-			if !item.Accepted {
+			if !item.Accepted || item.Negative {
 				continue
 			}
 
 		case "corrected":
-			if item.Accepted {
+			if item.Accepted || item.Negative {
+				continue
+			}
+
+		case "negative":
+			if !item.Negative {
 				continue
 			}
 		}
@@ -365,6 +374,9 @@ func trainingAnnotationMatchesQuery(item TrainingAnnotation, cleanQuery string)
 		item.Notes,
 		effective.SexPosition,
 	}
+	if item.Negative {
+		parts = append(parts, "negative negativ leer keine labels")
+	}
 
 	parts = append(parts, effective.PeoplePresent...)
 	parts = append(parts, effective.BodyPartsPresent...)
@@ -1875,6 +1887,10 @@ func trainingFrameHandler(w http.ResponseWriter, r *http.Request) {
 }
 
 func trainingDetectorBoxesForAnnotation(sample *TrainingSample, req TrainingFeedbackRequest) []TrainingBox {
+	if req.Negative {
+		return []TrainingBox{}
+	}
+
 	boxes := []TrainingBox{}
 
 	if req.Correction != nil {
@@ -1905,6 +1921,17 @@ func trainingDetectorBoxesForAnnotation(sample *TrainingSample, req TrainingFeed
 	return boxes
 }
 
+func trainingNegativeCorrection() *TrainingCorrection {
+	return &TrainingCorrection{
+		SexPosition:      "unknown",
+		PeoplePresent:    []string{},
+		BodyPartsPresent: []string{},
+		ObjectsPresent:   []string{},
+		ClothingPresent:  []string{},
+		Boxes:            []TrainingBox{},
+	}
+}
+
 func trainingFeedbackHandler(w http.ResponseWriter, r *http.Request) {
 	if r.Method != http.MethodPost {
 		trainingWriteError(w, http.StatusMethodNotAllowed, "method not allowed")
@@ -1922,6 +1949,10 @@ func trainingFeedbackHandler(w http.ResponseWriter, r *http.Request) {
 		trainingWriteError(w, http.StatusBadRequest, "sampleId missing")
 		return
 	}
+	if req.Negative {
+		req.Accepted = false
+		req.Correction = trainingNegativeCorrection()
+	}
 
 	root, err := trainingRootDir()
 	if err != nil {
@@ -1946,6 +1977,7 @@ func trainingFeedbackHandler(w http.ResponseWriter, r *http.Request) {
 		AnsweredAt:      time.Now().UTC().Format(time.RFC3339),
 		Prediction:      sample.Prediction,
 		Accepted:        req.Accepted,
+		Negative:        req.Negative,
 		Correction:      req.Correction,
 		Notes:           strings.TrimSpace(req.Notes),
 	}
@@ -1962,8 +1994,8 @@ func trainingFeedbackHandler(w http.ResponseWriter, r *http.Request) {
 
 	detectorBoxes := trainingDetectorBoxesForAnnotation(sample, req)
 
-	if len(detectorBoxes) > 0 {
+	if req.Negative || len(detectorBoxes) > 0 {
-		if err := trainingWriteDetectorSample(root, sample, detectorBoxes); err != nil {
+		if err := trainingWriteDetectorSample(root, sample, detectorBoxes, req.Negative); err != nil {
 			appLogln("⚠️ detector sample write failed:", err)
 		}
 	}
@@ -1987,6 +2019,10 @@ func trainingFeedbackUpdateHandler(w http.ResponseWriter, r *http.Request) {
 
 	req.SampleID = strings.TrimSpace(req.SampleID)
 	req.AnsweredAt = strings.TrimSpace(req.AnsweredAt)
+	if req.Negative {
+		req.Accepted = false
+		req.Correction = trainingNegativeCorrection()
+	}
 
 	if req.SampleID == "" {
 		trainingWriteError(w, http.StatusBadRequest, "sampleId missing")
@@ -2039,6 +2075,7 @@ func trainingFeedbackUpdateHandler(w http.ResponseWriter, r *http.Request) {
 
 	updated := old
 	updated.Accepted = req.Accepted
+	updated.Negative = req.Negative
 	updated.Notes = strings.TrimSpace(req.Notes)
 
 	if req.Accepted {
@@ -2073,12 +2110,13 @@ func trainingFeedbackUpdateHandler(w http.ResponseWriter, r *http.Request) {
 	detectorBoxes := trainingDetectorBoxesForAnnotation(sample, TrainingFeedbackRequest{
 		SampleID:   req.SampleID,
 		Accepted:   req.Accepted,
+		Negative:   req.Negative,
 		Correction: req.Correction,
 		Notes:      req.Notes,
 	})
 
-	if len(detectorBoxes) > 0 {
+	if req.Negative || len(detectorBoxes) > 0 {
-		if err := trainingWriteDetectorSample(root, sample, detectorBoxes); err != nil {
+		if err := trainingWriteDetectorSample(root, sample, detectorBoxes, req.Negative); err != nil {
 			appLogln("⚠️ detector sample update failed:", err)
 		}
 	}
@@ -2161,7 +2199,7 @@ func trainingHasDetectorTrainingData(imagesDir string, labelsDir string) bool {
 		id := strings.TrimSuffix(e.Name(), filepath.Ext(e.Name()))
 		labelPath := filepath.Join(labelsDir, id+".txt")
 
-		if fileExistsNonEmpty(labelPath) {
+		if fileExists(labelPath) {
 			count++
 		}
 	}
@@ -2227,17 +2265,23 @@ func trainingTrainHandler(w http.ResponseWriter, r *http.Request) {
 
 	trainCount := trainingCountDetectorSamples(detectorTrainImages, detectorTrainLabels)
 	valCount := trainingCountDetectorSamples(detectorValImages, detectorValLabels)
+	positiveTrainCount := trainingCountPositiveDetectorSamples(detectorTrainImages, detectorTrainLabels)
+	positiveValCount := trainingCountPositiveDetectorSamples(detectorValImages, detectorValLabels)
 
 	if !fileExistsNonEmpty(detectorDatasetYAML) ||
 		trainCount < minDetectorTrainCount ||
-		valCount < minDetectorValCount {
+		valCount < minDetectorValCount ||
+		positiveTrainCount == 0 ||
+		positiveValCount == 0 {
 		trainingWriteError(
 			w,
 			http.StatusBadRequest,
 			fmt.Sprintf(
-				"Zu wenige YOLO26-Box-Labels. Train=%d, Val=%d. Benötigt: mindestens %d Train und %d Val.",
+				"Zu wenige YOLO26-Beispiele. Train=%d (%d positiv), Val=%d (%d positiv). Benötigt: mindestens %d Train, %d Val und je ein positives Beispiel.",
 				trainCount,
+				positiveTrainCount,
 				valCount,
+				positiveValCount,
 				minDetectorTrainCount,
 				minDetectorValCount,
 			),
@@ -2258,6 +2302,8 @@ func trainingTrainHandler(w http.ResponseWriter, r *http.Request) {
 		"detector": map[string]any{
 			"trainCount":         trainCount,
 			"valCount":           valCount,
+			"positiveTrainCount": positiveTrainCount,
+			"positiveValCount":   positiveValCount,
 			"requiredTrain":      minDetectorTrainCount,
 			"requiredVal":        minDetectorValCount,
 			"datasetYAML":        detectorDatasetYAML,
@@ -2337,17 +2383,23 @@ func trainingRunJob(ctx context.Context, root string, count int) {
 
 	trainCount := trainingCountDetectorSamples(detectorTrainImages, detectorTrainLabels)
 	valCount := trainingCountDetectorSamples(detectorValImages, detectorValLabels)
+	positiveTrainCount := trainingCountPositiveDetectorSamples(detectorTrainImages, detectorTrainLabels)
+	positiveValCount := trainingCountPositiveDetectorSamples(detectorValImages, detectorValLabels)
 
 	fmt.Printf(
-		"🔎 detector data: train=%d val=%d yaml=%v\n",
+		"🔎 detector data: train=%d (%d positive) val=%d (%d positive) yaml=%v\n",
 		trainCount,
+		positiveTrainCount,
 		valCount,
+		positiveValCount,
 		fileExistsNonEmpty(detectorDatasetYAML),
 	)
 
 	if fileExistsNonEmpty(detectorDatasetYAML) &&
 		trainCount >= minDetectorTrainCount &&
-		valCount >= minDetectorValCount {
+		valCount >= minDetectorValCount &&
+		positiveTrainCount > 0 &&
+		positiveValCount > 0 {
 		trainingSetJobStatus(func(s *TrainingJobStatus) {
 			s.Progress = 15
 			s.Step = "YOLO26 Detector wird trainiert…"
@@ -2398,9 +2450,11 @@ func trainingRunJob(ctx context.Context, root string, count int) {
 	} else {
 		detectorStatus = "skipped_no_detector_data"
 		detectorOutput = fmt.Sprintf(
-			"YOLO26 Detector übersprungen: zu wenige Box-Labels. Train=%d, Val=%d. Benötigt: mindestens %d Train und %d Val.",
+			"YOLO26 Detector übersprungen: zu wenige Beispiele. Train=%d (%d positiv), Val=%d (%d positiv). Benötigt: mindestens %d Train, %d Val und je ein positives Beispiel.",
 			trainCount,
+			positiveTrainCount,
 			valCount,
+			positiveValCount,
 			minDetectorTrainCount,
 			minDetectorValCount,
 		)
@@ -2545,7 +2599,9 @@ func trainingBuildStats(root string) (*TrainingStatsResponse, error) {
 
 		stats.FeedbackCount++
 
-		if annotation.Accepted {
+		if annotation.Negative {
+			stats.NegativeCount++
+		} else if annotation.Accepted {
 			stats.AcceptedCount++
 		} else {
 			stats.CorrectedCount++
@@ -2643,6 +2699,10 @@ func trainingBuildStats(root string) (*TrainingStatsResponse, error) {
 }
 
 func trainingEffectiveCorrection(annotation TrainingAnnotation) TrainingCorrection {
+	if annotation.Negative {
+		return *trainingNegativeCorrection()
+	}
+
 	if annotation.Correction != nil {
 		return *annotation.Correction
 	}
@@ -2761,11 +2821,15 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
 
 	trainCount := trainingCountDetectorSamples(detectorTrainImages, detectorTrainLabels)
 	valCount := trainingCountDetectorSamples(detectorValImages, detectorValLabels)
+	positiveTrainCount := trainingCountPositiveDetectorSamples(detectorTrainImages, detectorTrainLabels)
+	positiveValCount := trainingCountPositiveDetectorSamples(detectorValImages, detectorValLabels)
 
 	datasetReady := fileExistsNonEmpty(detectorDatasetYAML)
 	detectorDataReady := datasetReady &&
 		trainCount >= minDetectorTrainCount &&
-		valCount >= minDetectorValCount
+		valCount >= minDetectorValCount &&
+		positiveTrainCount > 0 &&
+		positiveValCount > 0
 
 	canTrain := feedbackCount >= minTrainingFeedbackCount && detectorDataReady
 
@@ -2793,6 +2857,8 @@ func trainingStatusHandler(w http.ResponseWriter, r *http.Request) {
 
 			"trainCount":         trainCount,
 			"valCount":           valCount,
+			"positiveTrainCount": positiveTrainCount,
+			"positiveValCount":   positiveValCount,
 			"requiredTrain":      minDetectorTrainCount,
 			"requiredVal":        minDetectorValCount,
 
@@ -2955,16 +3021,17 @@ func trainingOverallConfidence(
 	// Viele "Passt so"-Antworten bedeuten, dass die Vorhersagen brauchbar sind.
 	// Bei 4/229 ist dieser Teil bewusst sehr niedrig.
 	agreementScore := 0.0
-	if feedbackCount > 0 {
+	decisionCount := acceptedCount + correctedCount
-		agreementScore = clamp01(float64(acceptedCount) / float64(feedbackCount))
+	if decisionCount > 0 {
+		agreementScore = clamp01(float64(acceptedCount) / float64(decisionCount))
 	}
 
 	// Korrekturquote als zusätzlicher Dämpfer.
 	// 98% korrigiert soll die Gesamt-Confidence sichtbar drücken,
 	// aber nicht alle gesammelten Daten entwerten.
 	correctionRate := 0.0
-	if feedbackCount > 0 {
+	if decisionCount > 0 {
-		correctionRate = clamp01(float64(correctedCount) / float64(feedbackCount))
+		correctionRate = clamp01(float64(correctedCount) / float64(decisionCount))
 	}
 
 	correctionPenalty := 1.0 - math.Min(0.45, correctionRate*0.45)
@@ -3065,6 +3132,42 @@ func trainingCountDetectorSamples(imagesDir string, labelsDir string) int {
 
 	count := 0
 
+	for _, e := range entries {
+		if e.IsDir() {
+			continue
+		}
+
+		ext := strings.ToLower(filepath.Ext(e.Name()))
+		if !imageExts[ext] {
+			continue
+		}
+
+		id := strings.TrimSuffix(e.Name(), filepath.Ext(e.Name()))
+		labelPath := filepath.Join(labelsDir, id+".txt")
+
+		if fileExists(labelPath) {
+			count++
+		}
+	}
+
+	return count
+}
+
+func trainingCountPositiveDetectorSamples(imagesDir string, labelsDir string) int {
+	imageExts := map[string]bool{
+		".jpg":  true,
+		".jpeg": true,
+		".png":  true,
+		".webp": true,
+	}
+
+	entries, err := os.ReadDir(imagesDir)
+	if err != nil {
+		return 0
+	}
+
+	count := 0
+
 	for _, e := range entries {
 		if e.IsDir() {
 			continue
@@ -4057,39 +4160,11 @@ func trainingApplyDetectorToPrediction(pred TrainingPrediction, det TrainingDete
 	return pred
 }
 
-func trainingWriteDetectorSample(root string, sample *TrainingSample, boxes []TrainingBox) error {
+func trainingDetectorLabelContent(
-	if sample == nil {
+	boxes []TrainingBox,
-		return errors.New("sample missing")
+	classMap map[string]int,
-	}
+	allowEmpty bool,
-
+) ([]byte, error) {
-	classMap, err := trainingDetectorClassMap()
-	if err != nil {
-		return err
-	}
-
-	srcFrame := filepath.Join(root, "frames", sample.SampleID+".jpg")
-	if _, err := os.Stat(srcFrame); err != nil {
-		return appErrorf("frame missing: %w", err)
-	}
-
-	// Stabiler 80/20 Split: gleicher sampleID landet immer im gleichen Split.
-	split := trainingStableSplit(sample.SampleID)
-
-	imgDir := filepath.Join(root, "detector", "dataset", "images", split)
-	lblDir := filepath.Join(root, "detector", "dataset", "labels", split)
-
-	if err := os.MkdirAll(imgDir, 0755); err != nil {
-		return err
-	}
-	if err := os.MkdirAll(lblDir, 0755); err != nil {
-		return err
-	}
-
-	dstFrame := filepath.Join(imgDir, sample.SampleID+".jpg")
-	if err := copyFile(srcFrame, dstFrame); err != nil {
-		return err
-	}
-
 	var lines []string
 
 	for _, box := range boxes {
@@ -4128,11 +4203,60 @@ func trainingWriteDetectorSample(root string, sample *TrainingSample, boxes []Tr
 	}
 
 	if len(lines) == 0 {
-		return errors.New("no valid detector boxes")
+		if allowEmpty {
+			return []byte{}, nil
+		}
+		return nil, errors.New("no valid detector boxes")
+	}
+
+	return []byte(strings.Join(lines, "\n") + "\n"), nil
+}
+
+func trainingWriteDetectorSample(
+	root string,
+	sample *TrainingSample,
+	boxes []TrainingBox,
+	allowEmpty bool,
+) error {
+	if sample == nil {
+		return errors.New("sample missing")
+	}
+
+	classMap, err := trainingDetectorClassMap()
+	if err != nil {
+		return err
+	}
+
+	labelContent, err := trainingDetectorLabelContent(boxes, classMap, allowEmpty)
+	if err != nil {
+		return err
+	}
+
+	srcFrame := filepath.Join(root, "frames", sample.SampleID+".jpg")
+	if _, err := os.Stat(srcFrame); err != nil {
+		return appErrorf("frame missing: %w", err)
+	}
+
+	// Stabiler 80/20 Split: gleicher sampleID landet immer im gleichen Split.
+	split := trainingStableSplit(sample.SampleID)
+
+	imgDir := filepath.Join(root, "detector", "dataset", "images", split)
+	lblDir := filepath.Join(root, "detector", "dataset", "labels", split)
+
+	if err := os.MkdirAll(imgDir, 0755); err != nil {
+		return err
+	}
+	if err := os.MkdirAll(lblDir, 0755); err != nil {
+		return err
+	}
+
+	dstFrame := filepath.Join(imgDir, sample.SampleID+".jpg")
+	if err := copyFile(srcFrame, dstFrame); err != nil {
+		return err
 	}
 
 	labelPath := filepath.Join(lblDir, sample.SampleID+".txt")
-	return os.WriteFile(labelPath, []byte(strings.Join(lines, "\n")+"\n"), 0644)
+	return os.WriteFile(labelPath, labelContent, 0644)
 }
 
 func trainingDeleteDetectorSample(root string, sampleID string) {
@@ -4160,11 +4284,13 @@ func trainingEnsureDetectorValidationSample(root string) error {
 	valLabels := filepath.Join(root, "detector", "dataset", "labels", "val")
 
 	currentVal := trainingCountDetectorSamples(valImages, valLabels)
-	if currentVal >= minDetectorValCount {
+	currentPositiveVal := trainingCountPositiveDetectorSamples(valImages, valLabels)
+	if currentVal >= minDetectorValCount && currentPositiveVal > 0 {
 		return nil
 	}
 
-	if trainingCountDetectorSamples(trainImages, trainLabels) < minDetectorTrainCount {
+	if trainingCountDetectorSamples(trainImages, trainLabels) < minDetectorTrainCount ||
+		trainingCountPositiveDetectorSamples(trainImages, trainLabels) == 0 {
 		return nil
 	}
 
@@ -4181,10 +4307,19 @@ func trainingEnsureDetectorValidationSample(root string) error {
 	}
 
 	copied := 0
-	needed := minDetectorValCount - currentVal
+	needed := max(0, minDetectorValCount-currentVal)
+	needsPositive := currentPositiveVal == 0
+
+	sort.SliceStable(entries, func(i, j int) bool {
+		iID := strings.TrimSuffix(entries[i].Name(), filepath.Ext(entries[i].Name()))
+		jID := strings.TrimSuffix(entries[j].Name(), filepath.Ext(entries[j].Name()))
+
+		return fileExistsNonEmpty(filepath.Join(trainLabels, iID+".txt")) &&
+			!fileExistsNonEmpty(filepath.Join(trainLabels, jID+".txt"))
+	})
 
 	for _, e := range entries {
-		if copied >= needed {
+		if copied >= needed && !needsPositive {
 			break
 		}
 
@@ -4202,14 +4337,14 @@ func trainingEnsureDetectorValidationSample(root string) error {
 		srcImage := filepath.Join(trainImages, e.Name())
 		srcLabel := filepath.Join(trainLabels, id+".txt")
 
-		if !fileExistsNonEmpty(srcImage) || !fileExistsNonEmpty(srcLabel) {
+		if !fileExistsNonEmpty(srcImage) || !fileExists(srcLabel) {
 			continue
 		}
 
 		dstImage := filepath.Join(valImages, e.Name())
 		dstLabel := filepath.Join(valLabels, id+".txt")
 
-		if fileExistsNonEmpty(dstImage) && fileExistsNonEmpty(dstLabel) {
+		if fileExistsNonEmpty(dstImage) && fileExists(dstLabel) {
 			continue
 		}
 
@@ -4221,6 +4356,9 @@ func trainingEnsureDetectorValidationSample(root string) error {
 		}
 
 		copied++
+		if fileExistsNonEmpty(srcLabel) {
+			needsPositive = false
+		}
 	}
 
 	return nil

backend/training_test.go

@@ -0,0 +1,133 @@
+package main
+
+import (
+	"fmt"
+	"os"
+	"path/filepath"
+	"strings"
+	"testing"
+)
+
+func TestTrainingDetectorLabelContentAllowsExplicitNegative(t *testing.T) {
+	content, err := trainingDetectorLabelContent(nil, map[string]int{"person": 0}, true)
+	if err != nil {
+		t.Fatalf("negative label content returned error: %v", err)
+	}
+	if len(content) != 0 {
+		t.Fatalf("negative label content = %q, want empty", string(content))
+	}
+}
+
+func TestTrainingDetectorLabelContentRejectsAccidentalEmptySample(t *testing.T) {
+	_, err := trainingDetectorLabelContent(
+		[]TrainingBox{{Label: "unknown_label", X: 0, Y: 0, W: 1, H: 1}},
+		map[string]int{"person": 0},
+		false,
+	)
+	if err == nil {
+		t.Fatal("invalid non-negative sample should be rejected")
+	}
+}
+
+func TestTrainingDetectorLabelContentWritesYOLOBox(t *testing.T) {
+	content, err := trainingDetectorLabelContent(
+		[]TrainingBox{{Label: "person", X: 0.1, Y: 0.2, W: 0.4, H: 0.6}},
+		map[string]int{"person": 3},
+		false,
+	)
+	if err != nil {
+		t.Fatalf("positive label content returned error: %v", err)
+	}
+
+	got := strings.TrimSpace(string(content))
+	want := "3 0.300000 0.500000 0.400000 0.600000"
+	if got != want {
+		t.Fatalf("label content = %q, want %q", got, want)
+	}
+}
+
+func TestTrainingCountDetectorSamplesIncludesEmptyLabels(t *testing.T) {
+	root := t.TempDir()
+	imagesDir := filepath.Join(root, "images")
+	labelsDir := filepath.Join(root, "labels")
+
+	if err := os.MkdirAll(imagesDir, 0755); err != nil {
+		t.Fatal(err)
+	}
+	if err := os.MkdirAll(labelsDir, 0755); err != nil {
+		t.Fatal(err)
+	}
+	if err := os.WriteFile(filepath.Join(imagesDir, "negative.jpg"), []byte("image"), 0644); err != nil {
+		t.Fatal(err)
+	}
+	if err := os.WriteFile(filepath.Join(labelsDir, "negative.txt"), []byte{}, 0644); err != nil {
+		t.Fatal(err)
+	}
+
+	if got := trainingCountDetectorSamples(imagesDir, labelsDir); got != 1 {
+		t.Fatalf("sample count = %d, want 1", got)
+	}
+	if got := trainingCountPositiveDetectorSamples(imagesDir, labelsDir); got != 0 {
+		t.Fatalf("positive sample count = %d, want 0", got)
+	}
+}
+
+func TestTrainingEnsureDetectorValidationSampleIncludesPositiveExample(t *testing.T) {
+	root := t.TempDir()
+	trainImages := filepath.Join(root, "detector", "dataset", "images", "train")
+	trainLabels := filepath.Join(root, "detector", "dataset", "labels", "train")
+
+	if err := os.MkdirAll(trainImages, 0755); err != nil {
+		t.Fatal(err)
+	}
+	if err := os.MkdirAll(trainLabels, 0755); err != nil {
+		t.Fatal(err)
+	}
+
+	for i := 0; i < minDetectorTrainCount; i++ {
+		id := fmt.Sprintf("sample-%02d", i)
+		if err := os.WriteFile(filepath.Join(trainImages, id+".jpg"), []byte("image"), 0644); err != nil {
+			t.Fatal(err)
+		}
+
+		label := []byte{}
+		if i == minDetectorTrainCount-1 {
+			label = []byte("0 0.5 0.5 1 1\n")
+		}
+		if err := os.WriteFile(filepath.Join(trainLabels, id+".txt"), label, 0644); err != nil {
+			t.Fatal(err)
+		}
+	}
+
+	if err := trainingEnsureDetectorValidationSample(root); err != nil {
+		t.Fatal(err)
+	}
+
+	valImages := filepath.Join(root, "detector", "dataset", "images", "val")
+	valLabels := filepath.Join(root, "detector", "dataset", "labels", "val")
+	if got := trainingCountDetectorSamples(valImages, valLabels); got < minDetectorValCount {
+		t.Fatalf("validation sample count = %d, want at least %d", got, minDetectorValCount)
+	}
+	if got := trainingCountPositiveDetectorSamples(valImages, valLabels); got < 1 {
+		t.Fatalf("positive validation sample count = %d, want at least 1", got)
+	}
+}
+
+func TestTrainingEffectiveCorrectionClearsNegativeAnnotation(t *testing.T) {
+	effective := trainingEffectiveCorrection(TrainingAnnotation{
+		Negative: true,
+		Prediction: TrainingPrediction{
+			SexPosition: "doggy",
+			Boxes: []TrainingBox{
+				{Label: "person", X: 0, Y: 0, W: 1, H: 1},
+			},
+		},
+	})
+
+	if effective.SexPosition != "unknown" {
+		t.Fatalf("sex position = %q, want unknown", effective.SexPosition)
+	}
+	if len(effective.Boxes) != 0 {
+		t.Fatalf("negative annotation has %d boxes, want 0", len(effective.Boxes))
+	}
+}

frontend/src/components/ui/TrainingFeedbackHistoryModal.tsx

@@ -67,11 +67,12 @@ export type TrainingFeedbackAnnotation = {
   answeredAt: string
   prediction: TrainingFeedbackPrediction
   accepted: boolean
+  negative?: boolean
   correction?: TrainingFeedbackCorrection
   notes?: string
 }
 
-type FeedbackFilter = 'all' | 'accepted' | 'corrected'
+type FeedbackFilter = 'all' | 'accepted' | 'corrected' | 'negative'
 
 function clamp01(value: number) {
   if (!Number.isFinite(value)) return 0
@@ -101,6 +102,17 @@ function normalizeBox(box: TrainingFeedbackBox): TrainingFeedbackBox {
 function annotationEffectiveCorrection(
   annotation: TrainingFeedbackAnnotation
 ): TrainingFeedbackCorrection {
+  if (annotation.negative) {
+    return {
+      sexPosition: 'unknown',
+      peoplePresent: [],
+      bodyPartsPresent: [],
+      objectsPresent: [],
+      clothingPresent: [],
+      boxes: [],
+    }
+  }
+
   if (annotation.correction) {
     return annotation.correction
   }
@@ -196,13 +208,18 @@ function frameUrlForHistory(item?: TrainingFeedbackAnnotation | null) {
   return `${item.frameUrl}${separator}history=1&t=${encodeURIComponent(item.sampleId)}`
 }
 
-function feedbackStatusClass(accepted: boolean) {
+function feedbackStatusClass(accepted: boolean, negative?: boolean) {
+  if (negative) {
+    return 'bg-blue-50 text-blue-800 ring-blue-200 dark:bg-blue-500/15 dark:text-blue-100 dark:ring-blue-400/30'
+  }
+
   return accepted
     ? 'bg-emerald-50 text-emerald-800 ring-emerald-200 dark:bg-emerald-500/15 dark:text-emerald-100 dark:ring-emerald-400/30'
     : 'bg-amber-50 text-amber-800 ring-amber-200 dark:bg-amber-500/15 dark:text-amber-100 dark:ring-amber-400/30'
 }
 
-function feedbackStatusText(accepted: boolean) {
+function feedbackStatusText(accepted: boolean, negative?: boolean) {
+  if (negative) return 'Negativbeispiel'
   return accepted ? 'Passt so' : 'Korrigiert'
 }
 
@@ -212,22 +229,31 @@ function labelText(value: string) {
 
 function StatusBadge(props: {
   accepted: boolean
+  negative?: boolean
   short?: boolean
 }) {
   return (
     <span
       className={[
         'inline-flex shrink-0 items-center gap-1.5 rounded-full px-2.5 py-1 text-xs font-medium ring-1',
-        feedbackStatusClass(props.accepted),
+        feedbackStatusClass(props.accepted, props.negative),
       ].join(' ')}
     >
-      {props.accepted ? (
+      {props.negative ? (
+        <CubeTransparentIcon className="h-3.5 w-3.5" aria-hidden="true" />
+      ) : props.accepted ? (
         <CheckCircleIcon className="h-3.5 w-3.5" aria-hidden="true" />
       ) : (
         <AdjustmentsHorizontalIcon className="h-3.5 w-3.5" aria-hidden="true" />
       )}
 
-      {props.short ? (props.accepted ? 'Passt' : 'Korr.') : feedbackStatusText(props.accepted)}
+      {props.short
+        ? props.negative
+          ? 'Negativ'
+          : props.accepted
+            ? 'Passt'
+            : 'Korr.'
+        : feedbackStatusText(props.accepted, props.negative)}
     </span>
   )
 }
@@ -646,7 +672,11 @@ function FeedbackListItem(props: {
               </div>
             </div>
 
-            <StatusBadge accepted={props.item.accepted} short />
+            <StatusBadge
+              accepted={props.item.accepted}
+              negative={props.item.negative}
+              short
+            />
           </div>
 
           <div className="mt-2 flex flex-wrap items-center gap-1.5 text-[11px] text-gray-500 dark:text-gray-400">
@@ -792,7 +822,10 @@ function CompactSourceCard(props: {
           </div>
         </div>
 
-        <StatusBadge accepted={props.item.accepted} />
+        <StatusBadge
+          accepted={props.item.accepted}
+          negative={props.item.negative}
+        />
       </div>
 
       <div className="mt-2.5 flex flex-wrap gap-1.5">
@@ -850,7 +883,11 @@ function ResultLabelsCard(props: {
           </div>
         </div>
 
-        <StatusBadge accepted={props.selected.accepted} short />
+        <StatusBadge
+          accepted={props.selected.accepted}
+          negative={props.selected.negative}
+          short
+        />
       </div>
 
       <div className="space-y-2 p-3">
@@ -926,8 +963,9 @@ export default function TrainingFeedbackHistoryModal(props: {
   const effective = selected ? annotationEffectiveCorrection(selected) : null
   const selectedImageSrc = frameUrlForHistory(selected)
 
-  const acceptedCount = props.items.filter((item) => item.accepted).length
+  const acceptedCount = props.items.filter((item) => item.accepted && !item.negative).length
-  const correctedCount = props.items.filter((item) => !item.accepted).length
+  const correctedCount = props.items.filter((item) => !item.accepted && !item.negative).length
+  const negativeCount = props.items.filter((item) => item.negative).length
   const shownCount = props.items.length
   const selectedBoxCount = effective?.boxes?.length ?? 0
 
@@ -941,8 +979,9 @@ export default function TrainingFeedbackHistoryModal(props: {
     return props.items
       .map((item, index) => ({ item, index }))
       .filter(({ item }) => {
-        if (filter === 'accepted' && !item.accepted) return false
+        if (filter === 'accepted' && (!item.accepted || item.negative)) return false
-        if (filter === 'corrected' && item.accepted) return false
+        if (filter === 'corrected' && (item.accepted || item.negative)) return false
+        if (filter === 'negative' && !item.negative) return false
 
         if (!cleanQuery) return true
 
@@ -953,6 +992,7 @@ export default function TrainingFeedbackHistoryModal(props: {
           item.sourceFile,
           item.sourcePath,
           item.notes,
+          item.negative ? 'negative negativ leer keine labels' : '',
           effectiveItem.sexPosition,
           ...effectiveItem.peoplePresent,
           ...effectiveItem.bodyPartsPresent,
@@ -1010,7 +1050,7 @@ export default function TrainingFeedbackHistoryModal(props: {
                 </div>
               </div>
 
-              <div className="grid grid-cols-3 gap-1.5 lg:flex lg:items-center">
+              <div className="grid grid-cols-4 gap-1.5 lg:flex lg:items-center">
                 <MetricTile
                   compact
                   label="Geladen"
@@ -1033,6 +1073,14 @@ export default function TrainingFeedbackHistoryModal(props: {
                   tone="amber"
                   icon={<AdjustmentsHorizontalIcon className="h-4 w-4" aria-hidden="true" />}
                 />
+
+                <MetricTile
+                  compact
+                  label="Negativ"
+                  value={negativeCount}
+                  tone="blue"
+                  icon={<CubeTransparentIcon className="h-4 w-4" aria-hidden="true" />}
+                />
               </div>
             </div>
           </div>
@@ -1119,7 +1167,7 @@ export default function TrainingFeedbackHistoryModal(props: {
                 />
               </div>
 
-              <div className="grid grid-cols-3 gap-1 overflow-visible rounded-2xl bg-gray-100 p-1 ring-1 ring-black/5 dark:bg-gray-800/80 dark:ring-white/10">
+              <div className="grid grid-cols-4 gap-1 overflow-visible rounded-2xl bg-gray-100 p-1 ring-1 ring-black/5 dark:bg-gray-800/80 dark:ring-white/10">
                 <FilterButton
                   active={filter === 'all'}
                   onClick={() => setFilter('all')}
@@ -1140,6 +1188,13 @@ export default function TrainingFeedbackHistoryModal(props: {
                 >
                   Korrigiert
                 </FilterButton>
+
+                <FilterButton
+                  active={filter === 'negative'}
+                  onClick={() => setFilter('negative')}
+                >
+                  Negativ
+                </FilterButton>
               </div>
             </div>
 
@@ -1223,7 +1278,10 @@ export default function TrainingFeedbackHistoryModal(props: {
                       ← Zurück
                     </button>
 
-                    <StatusBadge accepted={selected.accepted} />
+                    <StatusBadge
+                      accepted={selected.accepted}
+                      negative={selected.negative}
+                    />
                   </div>
                   
                   {props.onEditItem ? (

frontend/src/components/ui/TrainingTab.tsx

@@ -36,6 +36,8 @@ type ScoredLabel = {
 type TrainingDetectorStatus = {
   trainCount: number
   valCount: number
+  positiveTrainCount: number
+  positiveValCount: number
   requiredTrain: number
   requiredVal: number
   datasetReady: boolean
@@ -166,6 +168,7 @@ type TrainingStats = {
   feedbackCount: number
   acceptedCount: number
   correctedCount: number
+  negativeCount: number
   sampleCount: number
   boxCount: number
   modelAvailable: boolean
@@ -190,6 +193,7 @@ type TrainingAnnotation = {
   answeredAt: string
   prediction: TrainingPrediction
   accepted: boolean
+  negative?: boolean
   correction?: CorrectionState
   notes?: string
 }
@@ -204,7 +208,7 @@ type TrainingFeedbackListResponse = {
 }
 
 type TrainingSampleMode = 'random' | 'uncertain'
-type FeedbackFilter = 'all' | 'accepted' | 'corrected'
+type FeedbackFilter = 'all' | 'accepted' | 'corrected' | 'negative'
 
 function backendText(data: any, fallback: string) {
   return String(
@@ -587,6 +591,17 @@ function predictionToCorrection(sample: TrainingSample | null): CorrectionState
   }
 }
 
+function correctionHasRelevantContent(value: CorrectionState) {
+  return (
+    (value.sexPosition && value.sexPosition !== 'unknown') ||
+    (value.peoplePresent?.length ?? 0) > 0 ||
+    (value.bodyPartsPresent?.length ?? 0) > 0 ||
+    (value.objectsPresent?.length ?? 0) > 0 ||
+    (value.clothingPresent?.length ?? 0) > 0 ||
+    (value.boxes?.length ?? 0) > 0
+  )
+}
+
 function applyBoxLabelToCorrection(
   state: CorrectionState,
   label: string,
@@ -1585,6 +1600,7 @@ function TrainingStatsModal(props: {
 
   const acceptedCount = stats?.acceptedCount ?? 0
   const correctedCount = stats?.correctedCount ?? 0
+  const negativeCount = stats?.negativeCount ?? 0
   const totalFeedback = stats?.feedbackCount ?? props.feedbackCount
   const boxCount = stats?.boxCount ?? 0
   const sampleCount = stats?.sampleCount ?? 0
@@ -1717,7 +1733,7 @@ function TrainingStatsModal(props: {
                   />
                 </div>
 
-                <div className="mt-3 grid grid-cols-4 gap-1.5">
+                <div className="mt-3 grid grid-cols-5 gap-1.5">
                   <div className="rounded-xl bg-gray-50 px-2 py-1.5 text-center ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
                     <div className="text-[9px] font-semibold uppercase tracking-wide text-gray-500 dark:text-gray-400">
                       Feedback
@@ -1745,6 +1761,15 @@ function TrainingStatsModal(props: {
                     </div>
                   </div>
 
+                  <div className="rounded-xl bg-gray-50 px-2 py-1.5 text-center ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
+                    <div className="text-[9px] font-semibold uppercase tracking-wide text-gray-500 dark:text-gray-400">
+                      Negativ
+                    </div>
+                    <div className="mt-0.5 text-sm font-black text-blue-700 dark:text-blue-300">
+                      {negativeCount}
+                    </div>
+                  </div>
+
                   <div className="rounded-xl bg-gray-50 px-2 py-1.5 text-center ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
                     <div className="text-[9px] font-semibold uppercase tracking-wide text-gray-500 dark:text-gray-400">
                       Boxen
@@ -1758,7 +1783,7 @@ function TrainingStatsModal(props: {
             </div>
 
             {/* Desktop/Tablet: ausführliche Karten */}
-            <div className="hidden sm:grid sm:grid-cols-4 sm:gap-2">
+            <div className="hidden sm:grid sm:grid-cols-5 sm:gap-2">
               <div className="rounded-xl bg-gray-50 p-3 ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
                 <div className="text-[11px] font-medium uppercase tracking-wide text-gray-500 dark:text-gray-400">
                   Feedback
@@ -1795,6 +1820,18 @@ function TrainingStatsModal(props: {
                 </div>
               </div>
 
+              <div className="rounded-xl bg-gray-50 p-3 ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
+                <div className="text-[11px] font-medium uppercase tracking-wide text-gray-500 dark:text-gray-400">
+                  Negativ
+                </div>
+                <div className="mt-1 text-2xl font-bold text-blue-700 dark:text-blue-300">
+                  {negativeCount}
+                </div>
+                <div className="mt-1 text-[11px] text-gray-500 dark:text-gray-400">
+                  {countPercent(negativeCount, totalFeedback)}
+                </div>
+              </div>
+
               <div className="rounded-xl bg-gray-50 p-3 ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
                 <div className="text-[11px] font-medium uppercase tracking-wide text-gray-500 dark:text-gray-400">
                   Boxen
@@ -1974,6 +2011,17 @@ function annotationToTrainingSample(item: TrainingAnnotation): TrainingSample {
 }
 
 function annotationToCorrectionState(item: TrainingAnnotation): CorrectionState {
+  if (item.negative) {
+    return {
+      sexPosition: 'unknown',
+      peoplePresent: [],
+      bodyPartsPresent: [],
+      objectsPresent: [],
+      clothingPresent: [],
+      boxes: [],
+    }
+  }
+
   if (item.correction) {
     return item.correction
   }
@@ -1993,6 +2041,7 @@ export default function TrainingTab(props: {
   const [sample, setSample] = useState<TrainingSample | null>(null)
   const [correction, setCorrection] = useState<CorrectionState>(() => predictionToCorrection(null))
   const [hasManualCorrection, setHasManualCorrection] = useState(false)
+  const [negativeExample, setNegativeExample] = useState(false)
   const [loading, setLoading] = useState(false)
   const [analysisProgress, setAnalysisProgress] = useState(0)
   const [analysisStep, setAnalysisStep] = useState('')
@@ -2181,6 +2230,7 @@ export default function TrainingTab(props: {
     setSample(annotationToTrainingSample(item))
     setCorrection(annotationToCorrectionState(item))
     setHasManualCorrection(!item.accepted)
+    setNegativeExample(Boolean(item.negative))
 
     setEditingFeedback({
       sampleId: item.sampleId,
@@ -2419,6 +2469,8 @@ export default function TrainingTab(props: {
   }, [labels.people, labels.bodyParts, labels.objects, labels.clothing])
 
   const correctionBoxes = correction.boxes ?? []
+  const isNegativeCorrection =
+    negativeExample && !correctionHasRelevantContent(correction)
 
   const visibleBoxes = [
     ...correctionBoxes.map((box, index) => ({ box, index, isDraft: false })),
@@ -2629,6 +2681,8 @@ export default function TrainingTab(props: {
         ? {
             trainCount: Number(data.detector.trainCount ?? 0),
             valCount: Number(data.detector.valCount ?? 0),
+            positiveTrainCount: Number(data.detector.positiveTrainCount ?? 0),
+            positiveValCount: Number(data.detector.positiveValCount ?? 0),
             requiredTrain: Number(data.detector.requiredTrain ?? 20),
             requiredVal: Number(data.detector.requiredVal ?? 3),
             datasetReady: Boolean(data.detector.datasetReady),
@@ -2714,6 +2768,7 @@ export default function TrainingTab(props: {
     setSample(nextSample)
     setCorrection(nextCorrection)
     setHasManualCorrection(Boolean(opts?.manualCorrection))
+    setNegativeExample(false)
 
     const initiallyExpandedSection: CorrectionSectionKey | null =
       nextCorrection.sexPosition && nextCorrection.sexPosition !== 'unknown'
@@ -2998,6 +3053,7 @@ export default function TrainingTab(props: {
         feedbackCount: Number(data?.feedbackCount ?? 0),
         acceptedCount: Number(data?.acceptedCount ?? 0),
         correctedCount: Number(data?.correctedCount ?? 0),
+        negativeCount: Number(data?.negativeCount ?? 0),
         sampleCount: Number(data?.sampleCount ?? 0),
         boxCount: Number(data?.boxCount ?? 0),
         modelAvailable: Boolean(data?.modelAvailable),
@@ -3437,7 +3493,12 @@ export default function TrainingTab(props: {
   ])
 
   const saveFeedback = useCallback(
-    async (accepted: boolean) => {
+    async (
+      accepted: boolean,
+      options?: {
+        negative?: boolean
+      }
+    ) => {
       if (!sample) return
 
       setSaving(true)
@@ -3449,16 +3510,28 @@ export default function TrainingTab(props: {
           .map(normalizeBox)
           .filter((box) => box.label && box.w > 0 && box.h > 0)
 
-        const correctionPayload: CorrectionState = {
+        const negative = options?.negative ?? isNegativeCorrection
+        const correctionPayload: CorrectionState = negative
+          ? {
+              sexPosition: 'unknown',
+              peoplePresent: [],
+              bodyPartsPresent: [],
+              objectsPresent: [],
+              clothingPresent: [],
+              boxes: [],
+            }
+          : {
               ...correction,
               peoplePresent: peopleLabelsFromBoxes(normalizedBoxes, labelsRef.current),
               boxes: normalizedBoxes,
             }
+        const effectiveAccepted = negative ? false : accepted
 
         const payload = {
           sampleId: sample.sampleId,
-          accepted,
+          accepted: effectiveAccepted,
-          correction: accepted && correctionPayload.boxes.length === 0
+          negative,
+          correction: effectiveAccepted && correctionPayload.boxes.length === 0
             ? undefined
             : correctionPayload,
         }
@@ -3497,8 +3570,9 @@ export default function TrainingTab(props: {
               item.answeredAt === editingFeedback?.answeredAt
                 ? {
                     ...item,
-                    accepted,
+                    accepted: effectiveAccepted,
-                    correction: accepted ? undefined : correctionPayload,
+                    negative,
+                    correction: effectiveAccepted ? undefined : correctionPayload,
                   }
                 : item
             )
@@ -3507,10 +3581,14 @@ export default function TrainingTab(props: {
 
         setMessage(
           wasEditingFeedback
-            ? accepted
+            ? negative
+              ? 'Negativbeispiel aktualisiert.'
+              : effectiveAccepted
                 ? 'Feedback aktualisiert.'
                 : 'Korrektur aktualisiert.'
-            : accepted
+            : negative
+              ? 'Negativbeispiel gespeichert.'
+              : effectiveAccepted
                 ? 'Feedback gespeichert.'
                 : 'Korrektur gespeichert.'
         )
@@ -3540,6 +3618,7 @@ export default function TrainingTab(props: {
       sample,
       correction,
       editingFeedback,
+      isNegativeCorrection,
       loadNext,
       loadTrainingStatus,
       loadNextImportedQueuedSample,
@@ -3571,6 +3650,7 @@ export default function TrainingTab(props: {
       setSample(null)
       setCorrection(predictionToCorrection(null))
       setHasManualCorrection(false)
+      setNegativeExample(false)
       setDrawingBox(null)
       setBoxInteraction(null)
       setTouchMagnifier(null)
@@ -3685,6 +3765,7 @@ export default function TrainingTab(props: {
 
       setSample(null)
       setCorrection(predictionToCorrection(null))
+      setNegativeExample(false)
       setTrainingStatus({
         feedbackCount: 0,
         requiredCount,
@@ -4242,12 +4323,19 @@ export default function TrainingTab(props: {
       0,
       Number(detector?.requiredVal ?? 3) - Number(detector?.valCount ?? 0)
     )
+    const missingPositiveTrain = Number(detector?.positiveTrainCount ?? 0) < 1
+    const missingPositiveVal = Number(detector?.positiveValCount ?? 0) < 1
+    const positiveSamplesMissing = missingPositiveTrain || missingPositiveVal
     const statusText = trainingRunning
       ? shownTrainingStep || 'Training läuft…'
       : !feedbackReady
         ? `${Math.max(0, requiredCount - feedbackCount)} Feedback fehlen noch`
         : !detectorReady
-          ? `YOLO-Boxen fehlen: ${missingTrain} Train, ${missingVal} Val`
+          ? missingTrain > 0 || missingVal > 0
+            ? `YOLO-Beispiele fehlen: ${missingTrain} Train, ${missingVal} Val`
+            : positiveSamplesMissing
+              ? 'Je ein positives Beispiel in Train und Val erforderlich'
+              : 'YOLO-Datensatz noch nicht bereit'
           : canStartTraining
             ? 'Bereit zum Trainieren'
             : 'Noch nicht trainingsbereit'
@@ -4431,7 +4519,10 @@ export default function TrainingTab(props: {
                 </div>
 
                 <div className="mt-3 grid grid-cols-2 gap-2 text-[10px]">
-                  <div className="rounded-lg bg-white/70 px-2 py-1.5 ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
+                  <div
+                    className="rounded-lg bg-white/70 px-2 py-1.5 ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10"
+                    title={`${trainingStatus?.detector?.positiveTrainCount ?? 0} positive Trainingsbeispiele`}
+                  >
                     <div className="font-semibold opacity-60">
                       Dauer
                     </div>
@@ -4442,7 +4533,10 @@ export default function TrainingTab(props: {
                     </div>
                   </div>
 
-                  <div className="rounded-lg bg-white/70 px-2 py-1.5 ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10">
+                  <div
+                    className="rounded-lg bg-white/70 px-2 py-1.5 ring-1 ring-black/5 dark:bg-white/5 dark:ring-white/10"
+                    title={`${trainingStatus?.detector?.positiveValCount ?? 0} positive Validierungsbeispiele`}
+                  >
                     <div className="font-semibold opacity-60">
                       Feedback
                     </div>
@@ -4515,7 +4609,7 @@ export default function TrainingTab(props: {
                   : !feedbackReady
                     ? `Noch zu wenig Feedback: ${feedbackCount}/${requiredCount}.`
                     : !detectorReady
-                      ? `Noch zu wenige YOLO-Box-Labels: Train ${detector?.trainCount ?? 0}/${detector?.requiredTrain ?? 20}, Val ${detector?.valCount ?? 0}/${detector?.requiredVal ?? 3}.`
+                      ? `YOLO-Datensatz noch nicht bereit: Train ${detector?.trainCount ?? 0}/${detector?.requiredTrain ?? 20} (${detector?.positiveTrainCount ?? 0} positiv), Val ${detector?.valCount ?? 0}/${detector?.requiredVal ?? 3} (${detector?.positiveValCount ?? 0} positiv).`
                       : 'Training ist aktuell nicht verfügbar.'
             }
           >
@@ -5759,6 +5853,22 @@ export default function TrainingTab(props: {
                 </span>
               </Button>
 
+              <Button
+                size="md"
+                variant={isNegativeCorrection ? 'primary' : 'soft'}
+                color="blue"
+                disabled={uiLocked || frameBusy || !sample}
+                onClick={() => void saveFeedback(false, { negative: true })}
+                className="col-span-2 w-full justify-center px-2 text-xs sm:text-sm"
+                title="Speichert das Bild ausdrücklich ohne relevante Labels oder Boxen als YOLO-Negativbeispiel."
+              >
+                <span className="inline-flex items-center gap-1.5">
+                  <XCircleIcon className="h-3.5 w-3.5" aria-hidden="true" />
+                  <span className="sm:hidden">Negativbeispiel</span>
+                  <span className="hidden sm:inline">Keine relevanten Inhalte & weiter</span>
+                </span>
+              </Button>
+
               <div className="col-span-2 hidden lg:block">
                 <Button
                   size="md"