Problem in training Unet with multi class labels

Hi, I have a coco json annotation file with 3 classes.
I generate mask images from segmentation points in coco json, through the following code. Through the pillow putpalette each class in the mask images has a pixel value between 1 and 3. But the evaluation result is always nan.

def unet_convert_dataset(path_to_annotation_json, path_to_original_images_folder, path_to_temp_images_folder, path_to_masks_folder, palette=None, train=False):

    coco = COCO(path_to_annotation_json)

    cats = coco.loadCats(coco.getCatIds())

    categories = [cat['name'] for cat in cats]

    if train:
        palette = [0, 0, 0]
        for i in range(len(categories)):
            palette += ([random.randint(20, 255)] * 3)

    for img in coco.imgs:
        height, width = coco.imgs[img]['height'], coco.imgs[img]['width']
        mask = np.zeros((height, width))

        image_path = os.path.join(path_to_original_images_folder, coco.imgs[img]['file_name'])
        image = cv2.imread(image_path)
        image_name = coco.imgs[img]['file_name'].rsplit(".", 1)[0]
        cv2.imwrite(path_to_temp_images_folder + '/' + image_name + '.png', image)

        catIds = coco.getCatIds(catNms=cats)
        annIds = coco.getAnnIds(imgIds=[img], catIds=catIds)
        anns = coco.loadAnns(annIds)
        for i, ann in enumerate(anns):
            if ann['image_id'] == coco.imgs[img]['id']:
                x = []
                y = []
                for s in ann['segmentation']:
                    category = ann['category_id']

                    for k in range(0, len(s)):
                        if k % 2 == 0:
                            x.append(s[k])
                            y.append(s[k+1])
                    fill_row_coords, fill_col_coords = draw.polygon(y, x, (height, width))
                    mask[fill_row_coords, fill_col_coords] = category


                # Zero-pad the palette to 256 RGB colours, i.e. 768 values
                palette += (768-len(palette))*[0]
                # Make black and white (bw) PIL/Pillow image from the binary array
                bw = Image.fromarray(mask.astype(np.uint8))
                bw.convert("L")
                # Push the palette into image and save
                bw.putpalette(palette)

                bw.save(path_to_masks_folder + '/' + image_name + ".png")

evaluation result:

“{‘background’: {‘precision’: 0.9648212, ‘Recall’: 1.0, ‘F1 Score’: 0.9820956837153484, ‘iou’: 0.9648212}, ‘darz’: {‘precision’: nan, ‘Recall’: nan, ‘F1 Score’: nan, ‘iou’: nan}, ‘2color_fibers’: {‘precision’: nan, ‘Recall’: 0.0, ‘F1 Score’: nan, ‘iou’: 0.0}, ‘paregi’: {‘precision’: nan, ‘Recall’: 0.0, ‘F1 Score’: nan, ‘iou’: 0.0}}”

This my spec file:

random_seed: 42
model_config {
  model_input_width: 640
  model_input_height: 640
  model_input_channels: 3
  num_layers: 18
  all_projections: true
  arch: "resnet"
  freeze_blocks: 0
  freeze_blocks: 1
  use_batch_norm: True
  training_precision {
    backend_floatx: FLOAT32
  }
}

training_config {
  batch_size: 2
  epochs: 2
  log_summary_steps: 76
  checkpoint_interval: 5
  loss: "cross_entropy"
  learning_rate:0.0002
  regularizer {
    type: L2
    weight: 3e-09
  }
  optimizer {
    adam {
      epsilon: 9.99999993923e-09
      beta1: 0.899999976158
      beta2: 0.999000012875
    }
  }
}
dataset_config {
  dataset: "custom"
  augment: False
  augmentation_config {
    spatial_augmentation {
    hflip_probability : 0.5
    vflip_probability : 0.5
    crop_and_resize_prob : 0.5
    }
    brightness_augmentation {
      delta: 0.2
    }
  }
  input_image_type: "color"
  train_images_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/train/images/"
  train_masks_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/train/masks"

  val_images_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/val/images"
  val_masks_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/val/masks"

  test_images_path: "/workspace/results/fasterrcnn_carpet_resnet18/carpet_prod/val_coco/"

  data_class_config {
    target_classes {
      name: "background"
      mapping_class: "background"
      label_id: 0
    }
    target_classes {
      name: "darz"
      mapping_class: "darz"
      label_id: 1
    }  

    target_classes {
      name: "2color_fibers"
      mapping_class: "2color_fibers"
      label_id: 2
    }  

    target_classes {
      name: "paregi"
      mapping_class: "paregi"
      label_id: 3
    }  

  }
}

Refer to Problems encountered in training unet and inference unet - #26
and Multiple classes not detected? - #11 by Morganh
Please note that the pixel integer value should be equal to the value of the label_id provided in the spec.
UNet expects the images and corresponding masks encoded as images. Each mask image is a single-channel image, where every pixel is assigned an integer value that represents the segmentation class.

More, please try .png file.

I inserted the exact value of the pixels instead of 1 to 3 and they were detected during training. But the evaluation result is still nan.

2022-01-14 10:46:28,886 [INFO] iva.unet.model.utilities: Label Id 0: Train Id 0
2022-01-14 10:46:28,886 [INFO] iva.unet.model.utilities: Label Id 61: Train Id 1
2022-01-14 10:46:28,886 [INFO] iva.unet.model.utilities: Label Id 164: Train Id 3
2022-01-14 10:46:28,886 [INFO] iva.unet.model.utilities: Label Id 157: Train Id 2

Spec file:

random_seed: 42
model_config {
  model_input_width: 640
  model_input_height: 640
  model_input_channels: 3
  num_layers: 18
  all_projections: true
  arch: "resnet"
  freeze_blocks: 0
  freeze_blocks: 1
  use_batch_norm: True
  training_precision {
    backend_floatx: FLOAT32
  }
}

training_config {
  batch_size: 2
  epochs: 6
  log_summary_steps: 76
  checkpoint_interval: 5
  loss: "cross_entropy"
  learning_rate:0.0002
  regularizer {
    type: L2
    weight: 3e-09
  }
  optimizer {
    adam {
      epsilon: 9.99999993923e-09
      beta1: 0.899999976158
      beta2: 0.999000012875
    }
  }
}
dataset_config {
  dataset: "custom"
  augment: False
  augmentation_config {
    spatial_augmentation {
    hflip_probability : 0.5
    vflip_probability : 0.5
    crop_and_resize_prob : 0.5
    }
    brightness_augmentation {
      delta: 0.2
    }
  }
  input_image_type: "color"
  train_images_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/train/images/"
  train_masks_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/train/masks"

  val_images_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/val/images"
  val_masks_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/val/masks"

  test_images_path: ""

  data_class_config {
    target_classes {
      name: "background"
      mapping_class: "background"
      label_id: 0
    }
    target_classes {
      name: "darz"
      mapping_class: "darz"
      label_id: 61
    }  

    target_classes {
      name: "2color_fibers"
      mapping_class: "2color_fibers"
      label_id: 164
    }  

    target_classes {
      name: "paregi"
      mapping_class: "paregi"
      label_id: 157
    }  

  }
}

I converted all the mask images. So that the classes are labeled 0, 1, 2 and the background label is 3. But still the problem was not solved.

random_seed: 42
model_config {
  model_input_width: 640
  model_input_height: 640
  model_input_channels: 3
  num_layers: 18
  all_projections: true
  arch: "resnet"
  freeze_blocks: 0
  freeze_blocks: 1
  use_batch_norm: True
  training_precision {
    backend_floatx: FLOAT32
  }
}

training_config {
  batch_size: 2
  epochs: 6
  log_summary_steps: 76
  checkpoint_interval: 5
  loss: "cross_entropy"
  learning_rate:0.0002
  regularizer {
    type: L2
    weight: 3e-09
  }
  optimizer {
    adam {
      epsilon: 9.99999993923e-09
      beta1: 0.899999976158
      beta2: 0.999000012875
    }
  }
}
dataset_config {
  dataset: "custom"
  augment: False
  augmentation_config {
    spatial_augmentation {
    hflip_probability : 0.5
    vflip_probability : 0.5
    crop_and_resize_prob : 0.5
    }
    brightness_augmentation {
      delta: 0.2
    }
  }
  input_image_type: "color"
  train_images_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/train/images/"
  train_masks_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/train/masks"

  val_images_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/val/images"
  val_masks_path: "/workspace/results/unet_test/unpruned_model/carpet_prod_temp/val/masks"

  test_images_path: "/workspace/results/fasterrcnn_carpet_resnet18/carpet_prod/val_coco/"

  data_class_config {
    target_classes {
      name: "darz"
      mapping_class: "darz"
      label_id: 0
    }  

    target_classes {
      name: "2color_fibers"
      mapping_class: "2color_fibers"
      label_id: 1
    }  

    target_classes {
      name: "paregi"
      mapping_class: "paregi"
      label_id: 2
    }  

    target_classes {
      name: "background"
      mapping_class: "background"
      label_id: 3
    }
  }
}

@Morganh please help me.

Can you inspect each pixel value of one mask image? Can you share the result?

More, as mentioned in Problem in training unet - #21 by Morganh

  1. Please convert the mask images to gray images. After checking the public dataset you mentioned, the pixel value is either 0 or 128. Please map 128 to 1.
  2. For binary segmentation, the label_id should be 0 and 1. BTW, if there are 4 classes , label_id should belong to 0-3

I resized the image to 128 * 128 to make the numpy array smaller. Pixels with the number 3 are the background and pixel 2 in this image is one of the classes.

[[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]
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 [3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
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  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]]

Hi @Morganh
Could you please tell me what is wrong?

Should convert the mask image to a 1 channel image . Please check.
And in this mask image, every pixel that has the value of label_id.
The label_id should always start from 0 and go up in increasing order.