title: How to improve the accuracy of ssd mobilenet v2 coco using Tensorflow Object detection API
body: <p>I'm using the Tensorflow Object Detection API to create a custom object detector. I'm using the COCO trained models for transfer learning.</p>
  
  <p>I trained it using Faster Rcnn Resnet and got very accurate results, but the inference speed of this model is very slow. I tried training it with SSD mobilenet V2, which has very fast speed, but I'm getting very low accuracy with this model. Is there anything I can change in the config file to increase the accuracy of the model? Or will the SSD model not give very accurate results since it's a lightweight model? 
  Here's the config file I'm using right now. (I trained it using ~150 images and for 10000 steps)</p>
  
  <pre><code>  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
          reduce_boxes_in_lowest_layer: true
        }
      }
      image_resizer {
        fixed_shape_resizer {
          height: 900
          width: 400
        }
      }
      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: 3
          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
              }
            }
          }
        }
      }
      feature_extractor {
        type: 'ssd_inception_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,
          }
        }
        override_base_feature_extractor_hyperparams: true
      }
      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: 0
        }
        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: 12
    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: "/content/models/research/pretrained_model/model.ckpt"
    from_detection_checkpoint: true
    num_steps: 10000
    data_augmentation_options {
      random_horizontal_flip {
      }
    }
    data_augmentation_options {
      ssd_random_crop {
      }
    }
  }```
  </code></pre>