Overfitting in Tensorflow Object detection API

Question

I am training tensorflow object detection API model on the custom dataset i.e. License plate dataset. My goal is to deploy this model to the edge device using tensorflow lite so I can't use any RCNN family model. Because, I can't convert any RCNN family object detection model to tensorflow lite model (this is the limitation from tensorflow object detection API). I am using ssd_mobilenet_v2_coco model to train the custom dataset. Following is the code snippet of my config file:

model {
  ssd {
    num_classes: 1
    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'
      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: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/experiments/training_SSD/ssd_mobilenet_v2_coco_2018_03_29/model.ckpt"
  fine_tune_checkpoint_type:  "detection"
  num_steps: 150000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/training.record"
  }
  label_map_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/classes.pbtxt"
}

eval_config: {
  num_examples: 488
  num_visualizations : 488
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/testing.record"
  }
  label_map_path: "/home/sach/DL/Pycharm_Workspace/TF1.14/License_Plate_F-RCNN/dataset/records/classes.pbtxt"
  shuffle: false
  num_readers: 1
}

I have total 1932 images (train images: 1444 and val images: 448). I have trained the model for 150000 steps. Following is the output from tensorboard:

DetectionBoxes Precision [email protected] IOU: After 150K steps, the object detection model accuracy ([email protected] IOU) is ~0.97 i.e. 97%. Which seems to be fine at the moment.

Training Loss: After 150K steps, the training loss is ~1.3. This seems to be okay.

Evaluation/Validation Loss: After 150K steps, the evaluation/validation loss is ~3.90 which is pretty high. However, there is huge difference between training and evaluation loss. Is there any overfitting exist? How can I overcome this problem? In my point of view, training and evaluation loss should be close to each other.

• How can I reduce validation/evaluation loss?
• I am using the default config file so by default use_dropout: false. Should I change it to use_dropout: true in case overfitting exist?
• What should be the acceptable range of training and validation loss for object detection model?

Please share your views. Thanking you!

Answer 1

There are several reasons for overfitting problem In Neural networks, by looking at your config file, I would like to suggest a few things to try to avoid overfitting.

use_dropout: true so that it makes the Neurons less sensitive to minor changes in the weights.

Try increasing iou_threshold in batch_non_max_suppression.

Use l1 regularizer or combination of l1 and l2 regularizer.

Change the optimizer to Nadam or Adam Optimizers.

Include more Augmentation techniques.

You can also use Early Stopping to track your accuracy.

Alternatively, you can observe the Tensorboard visualization, take the weights before the step where the validation loss starts increasing.

I hope trying these steps will resolve the overfitting issue of your model.