diff --git a/netDetectorResNet50_stepthree.mat b/netDetectorResNet50_stepthree.mat
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diff --git a/test_stepthree.m b/test_stepthree.m
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+% Testscript um ein  Bild aus den Daten durch das RCCN_NET mit direkter
+% klassifizierung laufen zu lassen
+
+close all;
+clear;
+
+%die netze mit besserer Erkennung _2
+RCCN_NET = 'netDetectorResNet50_stepthree.mat';
+inputSize = [224 224 3];
+
+% first we need the data...
+dataDir = 'Picturedata'; % Destination-Folder for provided (img) Data
+zippedDataFile = 'PicturesResizedLabelsResizedSignsCutted.zip'; %Data provided by TA
+grDataFile = 'signDatasetGroundTruth.mat';
+func_setupData(dataDir, zippedDataFile, grDataFile);
+
+%load data
+grdata = load(grDataFile);
+traficSignDataset = grdata.DataSet;
+
+%Random Index
+%shuffledIndices = randperm(height(traficSignDataset));
+%testindx = shuffledIndices(1)
+%for testindx = 50:200
+testindx = 125;
+
+% Bild einlesen
+imgname = traficSignDataset.imageFilename{testindx}
+I = imresize(imread(imgname),inputSize(1:2));
+
+%RCCN-Detector laden
+pretrained = load(RCCN_NET);
+detector = pretrained.detector;
+
+[bbox, score, label] = detect(detector, I, 'MiniBatchSize', 32);
+
+
+sfigTitle = ""
+bdetected = height(bbox) > 0;
+if bdetected
+    I = insertObjectAnnotation(I,'rectangle',bbox,score);
+    sfigTitle = "Detected" + string(label);
+    
+else
+    sfigTitle = "Not Detected"
+end
+
+%end %end forschleife testindex
+
+figure;
+imshow(I);
+annotation('textbox', [0.5, 0.2, 0.1, 0.1], 'String', sfigTitle)
+
+%ggf. bild zuschneiden 
+if bdetected
+   icrop = imcrop(I , bbox);
+   figure;
+   imshow(icrop);
+   
+end
+
+
+
+
+    
+   
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