MobileNet-SSD 模型训练配置文件参数解析

2019年4月23日 851 次阅读 0 条评论 0 人点赞

文件名:ssd_mobilenet_v1_pets.config
路径:/path/to/models/research/object_detection/
models 下载地址:Github - TensorFlow Models

model {
    ssd {
        // 类别数,不包括 background
        num_classes: 20
        // 原文:Scales location targets as used in paper for joint training
        box_coder {
            faster_rcnn_box_coder {
                y_scale: 10.0
                x_scale: 10.0
                height_scale: 5.0
                width_scale: 5.0
            }
        }
        // 匹配规则
        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
            }
        }
        // 区域相似度度量规则,原文 region_similarity_calculator
        similarity_calculator {
            iou_similarity {}
        }
        // 预测用的 defaut_boxes
        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 {
                // 输入图片高度
                height: 300
                // 输入图片宽度
                width: 300
            }
        }
        // 卷积预测层模块参数,原文 ssd_box_predictor
        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,
                    }
                }
            }
        }
        // 特征提取
        feature_extractor {
            type: 'ssd_mobilenet_v1'
            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 {
            // 损失函数
            classification_loss {
                weighted_sigmoid {}
            }
            // 损失函数
            localization_loss {
                weighted_smooth_l1 {}
            }
            // 难样本挖掘规则
            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 {
            // 图像后处理,只用于验证,不参与训练流程
            batch_non_max_suppression {
                score_threshold: 1e-8
                iou_threshold: 0.6
                max_detections_per_class: 100
                max_total_detections: 100
            }
            // 图像后处理,只用于验证,不参与训练流程
            score_converter: SIGMOID
        }
    }
}

train_config: {
    // 每迭代一次输入的图片数量
    batch_size: 16
    optimizer {
        rms_prop_optimizer: {
            learning_rate: {
                exponential_decay_learning_rate {
                    initial_learning_rate: 0.001
                    decay_steps: 40000
                    decay_factor: 0.1
                }
            }
            momentum_optimizer_value: 0.9
            decay: 0.005
            epsilon: 1.0
        }
    }
    fine_tune_checkpoint: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/ssd_mobilenet/model.ckpt"
    from_detection_checkpoint: true
    load_all_detection_checkpoint_vars: true
    // 训练迭代次数
    num_steps: 50200
    data_augmentation_options {
        random_horizontal_flip {}
    }
    data_augmentation_options {
        ssd_random_crop {}
    }
}

train_input_reader: {
    tf_record_input_reader {
        input_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_train.record"
    }
    label_map_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_label_map.pbtxt"
}

eval_config: {
    metrics_set: "coco_detection_metrics"
    // 验证集中图片数量
    num_examples: 1100
}

eval_input_reader: {
    tf_record_input_reader {
        input_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_val.record"
    }
    label_map_path: "/usr/local/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/object_detection/ssd_model/pascal_label_map.pbtxt"
    shuffle: false
    num_readers: 1
}
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最后编辑:2019年9月21日

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