input_image_meta shape error while using pixellib custom trainig on images

Question

I am using pixellib fot training custom image instance segmentation. I have created a dataset whiche can be seen below in link. Dataset:https://drive.google.com/drive/folders/1MjpDNZtzGRNxEtCDcTmrjUuB1ics_3Jk?usp=sharing the code which I used to make a custom model is

import pixellib
from pixellib.custom_train import instance_custom_training

train_maskrcnn = instance_custom_training()

train_maskrcnn.modelConfig(network_backbone = "resnet101", num_classes= 2, batch_size = 4)

train_maskrcnn.load_pretrained_model("/content/drive/MyDrive/AI ML Trainee/Damage Detection/pix/mask_rcnn_coco.h5")

train_maskrcnn.load_dataset("/content/drive/MyDrive/AI ML Trainee/Damage Detection/pix/Dataset")

train_maskrcnn.train_model(num_epochs = 300, augmentation=True,  path_trained_models = "mask_rcnn_models")

the console output which I get is below :

Using resnet101 as network backbone For Mask R-CNN model
Applying Default Augmentation on Dataset
Train 48 images
Validate 0 images
Checkpoint Path: /content/mask_rcnn_models
Selecting layers to train
Epoch 1/300
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-c2bd46bd70ab> in <module>()
     7 train_maskrcnn.load_pretrained_model("/content/drive/MyDrive/AI ML Trainee/Damage Detection/pix/mask_rcnn_coco.h5")
     8 train_maskrcnn.load_dataset("/content/drive/MyDrive/AI ML Trainee/Damage Detection/pix/Dataset")
----> 9 train_maskrcnn.train_model(num_epochs = 300, augmentation=True,  path_trained_models = "mask_rcnn_models")

8 frames
/usr/local/lib/python3.7/dist-packages/pixellib/custom_train.py in train_model(self, num_epochs, path_trained_models, layers, augmentation)
   122 
   123         self.model.train(self.dataset_train, self.dataset_test,models = path_trained_models, augmentation = augmentation, 
--> 124         epochs=num_epochs,layers=layers)
   125 
   126 

/usr/local/lib/python3.7/dist-packages/pixellib/mask_rcnn.py in train(self, train_dataset, val_dataset, epochs, layers, models, augmentation, no_augmentation_sources)
  2316             max_queue_size=100,
  2317             workers=workers,
-> 2318             verbose = 1
  2319 
  2320         )

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_v1.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
   806         max_queue_size=max_queue_size,
   807         workers=workers,
--> 808         use_multiprocessing=use_multiprocessing)
   809 
   810   def evaluate(self,

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_generator_v1.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing)
   591         shuffle=shuffle,
   592         initial_epoch=initial_epoch,
--> 593         steps_name='steps_per_epoch')
   594 
   595   def evaluate(self,

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_generator_v1.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)
   257 
   258       is_deferred = not model._is_compiled
--> 259       batch_outs = batch_function(*batch_data)
   260       if not isinstance(batch_outs, list):
   261         batch_outs = [batch_outs]

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_v1.py in train_on_batch(self, x, y, sample_weight, class_weight, reset_metrics)
  1061     x, y, sample_weights = self._standardize_user_data(
  1062         x, y, sample_weight=sample_weight, class_weight=class_weight,
-> 1063         extract_tensors_from_dataset=True)
  1064 
  1065     # If `self._distribution_strategy` is True, then we are in a replica context

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_v1.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
  2334         is_dataset=is_dataset,
  2335         class_weight=class_weight,
-> 2336         batch_size=batch_size)
  2337 
  2338   def _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs,

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_v1.py in _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight, batch_size)
  2361           feed_input_shapes,
  2362           check_batch_axis=False,  # Don't enforce the batch size.
-> 2363           exception_prefix='input')
  2364 
  2365     # Get typespecs for the input data and sanitize it if necessary.

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_utils_v1.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
   538                              ': expected ' + names[i] + ' to have shape ' +
   539                              str(shape) + ' but got array with shape ' +
--> 540                              str(data_shape))
   541   return data
   542 

ValueError: Error when checking input: expected input_image_meta to have shape (15,) but got array with shape (14,)

I check my dataset every image has one annotation

I feel something is wrong with my dataset. Any help is appreciated.

Answer 1

Okay, this error is solved, I went to the pixellib library and according to them, we need validation data too in order to run the model. So I added validation data, (just a few images) and the library is functioning perfectly.

Sorry for the trouble.