diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 340d6bd..def1d0c 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -24,7 +24,7 @@ PREDICT_ONLY_ON_WHOLE_TRAINING_SET = False
 
 setting = ','.join([escapeFilePath(imageCameraFolder) for imageCameraFolder in IMAGES_CAMERAS_FOLDER]) + f'_{DENOISER}'
 
-imagesCamerasFileNames = {camera: os.listdir(imageCameraFolder) for camera, imageCameraFolder in IMAGES_CAMERAS_FOLDER.items()}
+imagesCamerasFileNames = {camera: [imageCameraFile for imageCameraFile in os.listdir(imageCameraFolder) if imageCameraFile.endswith('.NEF') or imageCameraFile.endswith('.ARW')] for camera, imageCameraFolder in IMAGES_CAMERAS_FOLDER.items()}
 # Fix randomness for reproducibility.
 random.seed(0)
 # Randomize order to not have a bias (chronological for instance) when split to make training and testing sets.
@@ -104,15 +104,20 @@ from utils import silentTqdm
 returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
 
 # Assume to have `{min,max}Color` hardcoded.
-print('Load training images to memory')
+# Can just load to memory `getSingleColorChannelImages`, see [Robust_image_source_identification_on_modern_smartphones/issues/62#issuecomment-1861](https://gitea.lemnoslife.com/Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones/issues/62#issuecomment-1861).
 rescaleIfNeeded = rescaleRawImageForDenoiser
 cameraTrainingImages = {}
-for cameraTrainingImageIndex in tqdm(range(numberOfTrainingImages), 'Camera training image index'):
-    for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
+for cameraTrainingImageIndex in tqdm(range(numberOfTrainingImages), 'Load to memory camera training image'):
+    for cameraIndex, camera in enumerate(IMAGES_CAMERAS_FOLDER):
         singleColorChannelImages = getSingleColorChannelImages(camera, cameraTrainingImageIndex)
         multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
         cameraTrainingImages[camera] = cameraTrainingImages.get(camera, []) + [multipleColorsImage]
-print('Training images loaded to memory')
+
+singleColorChannelTestingImages = {camera: [] for camera in IMAGES_CAMERAS_FOLDER}
+for camera in IMAGES_CAMERAS_FOLDER:
+    for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Load to memory camera testing image'):
+        singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+        singleColorChannelTestingImages[camera] += [singleColorChannelImages]
 
 # 2 loops:
 # - the first one is about computing `{min,max}Color`
@@ -128,7 +133,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                 for cameraTestingImageIndex in tqdm(range(numberOfTestingImages), 'Camera testing image index'):
                     print(f'{camera=} {numberOfTrainingImages + cameraTestingImageIndex=}')
 
-                    singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+                    singleColorChannelImages = singleColorChannelTestingImages[camera][cameraTestingImageIndex]#getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
                     multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
 
                     imagePrnuEstimateNpArray = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
@@ -171,7 +176,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
                             if DENOISER != Denoiser.MEAN:
                                 cameraTestingImageNoise = cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex]
                             else:
-                                singleColorChannelImages = getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
+                                singleColorChannelImages = singleColorChannelTestingImages[camera][cameraTestingImageIndex]#getSingleColorChannelImages(camera, numberOfTrainingImages + cameraTestingImageIndex)
                                 multipleColorsImage = getMultipleColorsImage(singleColorChannelImages)
                                 cameraTestingImageNoise = getImagePrnuEstimateNpArray(singleColorChannelImages, multipleColorsImage, camera)
 
diff --git a/datasets/raise/utils.py b/datasets/raise/utils.py
index d38fdea..1250784 100644
--- a/datasets/raise/utils.py
+++ b/datasets/raise/utils.py
@@ -90,11 +90,9 @@ def isARawImage(imageFilePath):
 
 def getColorChannel(imageFilePath, color):
     if isARawImage(imageFilePath):
-        numpyFilePath = f'{imageFilePath}.npy'
+        numpyFilePath = f'{imageFilePath}.{color}.npy'
         if os.path.isfile(numpyFilePath):
-            imageNpArray = np.load(numpyFilePath, allow_pickle = True).item()#[color]
-            print(imageNpArray)
-            #exit(1)
+            imageNpArray = np.load(numpyFilePath)
         else:
             with rawpy.imread(imageFilePath) as raw:
                 imageNpArray = getRawColorChannel(raw, color)