yolov3-tf2 ValueError: Shapes (18,) and (255,) are incompatible

python train.py --dataset ./data/train.tfrecord --val_dataset ./data/_val.tfrecord --classes ./data/coco2.names --num_classes 1 --mode fit --transfer darknet --batch_size 16 --epochs 3 --weights ./checkpoints/yolov3.tf --weights_num_classes 1

Not able to retrain the model with custom dataset. While running the model I am getting the following error.

2020-05-26 00:14:43.973851: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0: N 2020-05-26 00:14:43.977913: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4702 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci b us id: 0000:01:00.0, compute capability: 6.1) Traceback (most recent call last): File "train.py", line 193, in app.run(main) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\absl\app.py", line 299, in run _run_main(main, args) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\absl\app.py", line 250, in _run_main sys.exit(main(argv)) File "train.py", line 97, in main model_pretrained.load_weights(FLAGS.weights) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 234, in load_weights return super(Model, self).load_weights(filepath, by_name, skip_mismatch) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 1193, in load_weights status = self._trackable_saver.restore(filepath) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\training\tracking\util.py", line 1283, in restore checkpoint=checkpoint, proto_id=0).restore(self._graph_view.root) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\training\tracking\base.py", line 209, in restore restore_ops = trackable._restore_from_checkpoint_position(self) # pylint: disable=protected-access File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\training\tracking\base.py", line 908, in _restore_from_checkpoint_position tensor_saveables, python_saveables)) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\training\tracking\util.py", line 289, in restore_saveables validated_saveables).restore(self.save_path_tensor) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\training\saving\functional_saver.py", line 255, in restore restore_ops.update(saver.restore(file_prefix)) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\training\saving\functional_saver.py", line 102, in restore restored_tensors, restored_shapes=None) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\training\saving\saveable_object_util.py", line 116, in restore self.handle_op, self._var_shape, restored_tensor) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py", line 297, in shape_safe_assign_variable_handle shape.assert_is_compatible_with(value_tensor.shape) File "D:\Anaconda3\envs\yolov3-tf2-gpu\lib\site-packages\tensorflow_core\python\framework\tensor_shape.py", line 1110, in assert_is_compatible_with raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (18,) and (255,) are incompatible WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-8 W0526 00:14:52.688719 4204 util.py:144] Unresolved object in checkpoint: (root).layer-8 WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-9 W0526 00:14:52.688719 4204 util.py:144] Unresolved object in checkpoint: (root).layer-9 WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-10 W0526 00:14:52.689668 4204 util.py:144] Unresolved object in checkpoint: (root).layer-10 WARNING:tensorflow:Unresolved object in checkpoint: (root).layer-11 W0526 00:14:52.689668 4204 util.py:144] Unresolved object in checkpoint: (root).layer-11 WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e. g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. W0526 00:14:52.689668 4204 util.py:152] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.