Issue while retraining ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03

Hello, sorry for the inconvenience but I have currently the same issue. I'm using Tensorflow 2.5.0 with the right CUDA version, my model is ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03 and so I modified the associated config file : ssd_mobilenet_v2_quantized_300x300_coco.config (from https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v2_quantized_300x300_coco.config)

I did put the right path for me that is :
fine_tune_checkpoint: "C:/tensorflow1/models/research/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt"
I also have my own labelmap.pbtxt, my train.record and test.record
In the model folder I have the following files :
model.ckpt.data-00000-of-00001
model.ckpt.index
model.ckpt.meta
pipeline.config
tflite_graph.pb
tflite_graph.pbtxt

So i also modified the required path in the "pipeline.config" file.
I'm investigating since yesterday, so of course I googled it, but I did not find anything useful online to solve my error unfortunately.

And I also changed the line 83 :
type: 'ssd_mobilenet_v2' to type: 'ssd_mobilenet_v2_keras' because I also got an error from this on the default config file.

When I launch the following command :

python model_main_tf2.py --pipeline_config_path=training/ssd_mobilenet_v2_quantized_300x300_coco.config --model_dir=training --alsologtostderr

But indeed, this is telling me the same error as the top of this topic :

Traceback (most recent call last):
    File "model_main_tf2.py", line 115, in <module>
      tf.compat.v1.app.run()
    File "C:\Users\Flo\mob1\lib\site-packages\tensorflow\python\platform\app.py", line 40, in run
      _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
    File "C:\Users\Flo\mob1\lib\site-packages\absl\app.py", line 312, in run
      _run_main(main, args)
    File "C:\Users\Flo\mob1\lib\site-packages\absl\app.py", line 258, in _run_main
      sys.exit(main(argv))
    File "model_main_tf2.py", line 112, in main
      record_summaries=FLAGS.record_summaries)
    File "C:\tensorflow1\models\research\object_detection\model_lib_v2.py", line 603, in train_loop
      train_input, unpad_groundtruth_tensors)
    File "C:\tensorflow1\models\research\object_detection\model_lib_v2.py", line 389, in load_fine_tune_checkpoint
      raise IOError('Checkpoint is expected to be an object-based checkpoint.')
  OSError: Checkpoint is expected to be an object-based checkpoint.

My entire config file here :

# Quantized trained SSD with Mobilenet v2 on MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  ssd {
    num_classes: 15
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    anchor_generator {
      ssd_anchor_generator {
        num_layers: 6
        min_scale: 0.2
        max_scale: 0.95
        aspect_ratios: 1.0
        aspect_ratios: 2.0
        aspect_ratios: 0.5
        aspect_ratios: 3.0
        aspect_ratios: 0.3333
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 300
        width: 300
      }
    }
    box_predictor {
      convolutional_box_predictor {
        min_depth: 0
        max_depth: 0
        num_layers_before_predictor: 0
        use_dropout: false
        dropout_keep_probability: 0.8
        kernel_size: 1
        box_code_size: 4
        apply_sigmoid_to_scores: false
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.00004
            }
          }
          initializer {
            truncated_normal_initializer {
              stddev: 0.03
              mean: 0.0
            }
          }
          batch_norm {
            train: true,
            scale: true,
            center: true,
            decay: 0.9997,
            epsilon: 0.001,
          }
        }
      }
    }
    feature_extractor {
      type: 'ssd_mobilenet_v2_keras'
      min_depth: 16
      depth_multiplier: 1.0
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.00004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          train: true,
          scale: true,
          center: true,
          decay: 0.9997,
          epsilon: 0.001,
        }
      }
    }
    loss {
      classification_loss {
        weighted_sigmoid {
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      hard_example_miner {
        num_hard_examples: 3000
        iou_threshold: 0.99
        loss_type: CLASSIFICATION
        max_negatives_per_positive: 3
        min_negatives_per_image: 3
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  batch_size: 24
  optimizer {
    rms_prop_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.004
          decay_steps: 800720
          decay_factor: 0.95
        }
      }
      momentum_optimizer_value: 0.9
      decay: 0.9
      epsilon: 1.0
    }
  }
  fine_tune_checkpoint: "C:/tensorflow1/models/research/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03/model.ckpt"
  fine_tune_checkpoint_type:  "detection"
  # Note: The below line limits the training process to 200K steps, which we
  # empirically found to be sufficient enough to train the pets dataset. This
  # effectively bypasses the learning rate schedule (the learning rate will
  # never decay). Remove the below line to train indefinitely.
  num_steps: 200000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "C:/tensorflow1/models/research/object_detection/train.record"
  }
  label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
}

eval_config: {
  num_examples: 8000
  # Note: The below line limits the evaluation process to 10 evaluations.
  # Remove the below line to evaluate indefinitely.
  max_evals: 10
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "C:/tensorflow1/models/research/object_detection/test.record"
  }
  label_map_path: "C:/tensorflow1/models/research/object_detection/training/labelmap.pbtxt"
  shuffle: false
  num_readers: 1
}

graph_rewriter {
  quantization {
    delay: 48000
    weight_bits: 8
    activation_bits: 8
  }
}

Originally posted by @Drisnor in https://github.com/tensorflow/models/issues/9278#issuecomment-871459469