1.环境配置
2.数据集获取
3.训练集获取
4.训练
5.调用测试训练结果
6.代码讲解
本文是第四篇，下载预训练模型并训练自己的数据集。
前面我们配置好了labelmap，下面我们开始下载训练好的模型。
http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz
下载下来解压，然后我们配置下pipeline文件
需要改动的地方有
num_classes：这个是我们的分类数，我们只有red和blue就填2
batch_size:这里我填的是2，batch_size过大，每次放入内存中训练的数据就会越多，如果你的内存不够大且数据量比较小，就填小点，我的是8G内存，图片也不过一两千张。
initial_learning_rate：学习速率，可以不修改。
fine_tune_checkpoint：输入我们下载的模型的ckpt文件的绝对路径
label_map_path：配置好的labelmap的绝对路径
tf_record_input_reader的input_path：填之前生成好的tfrecord文件的绝对路径
我的配置为以下文件：
model { ?ssd { ???num_classes: 2 ???image_resizer { ?????fixed_shape_resizer { ???????height: 300 ???????width: 300 ?????} ???} ???feature_extractor { ?????type: "ssd_mobilenet_v2" ?????depth_multiplier: 1.0 ?????min_depth: 16 ?????conv_hyperparams { ???????regularizer { ?????????l2_regularizer { ???????????weight: 3.99999989895e-05 ?????????} ???????} ???????initializer { ?????????truncated_normal_initializer { ???????????mean: 0.0 ???????????stddev: 0.0299999993294 ?????????} ???????} ???????activation: RELU_6 ???????batch_norm { ?????????decay: 0.999700009823 ?????????center: true ?????????scale: true ?????????epsilon: 0.0010000000475 ?????????train: true ???????} ?????} ?????use_depthwise: true ???} ???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 { ?????} ???} ???box_predictor { ?????convolutional_box_predictor { ???????conv_hyperparams { ?????????regularizer { ???????????l2_regularizer { ?????????????weight: 3.99999989895e-05 ???????????} ?????????} ?????????initializer { ???????????truncated_normal_initializer { ?????????????mean: 0.0 ?????????????stddev: 0.0299999993294 ???????????} ?????????} ?????????activation: RELU_6 ?????????batch_norm { ???????????decay: 0.999700009823 ???????????center: true ???????????scale: true ???????????epsilon: 0.0010000000475 ???????????train: true ?????????} ???????} ???????min_depth: 0 ???????max_depth: 0 ???????num_layers_before_predictor: 0 ???????use_dropout: false ???????dropout_keep_probability: 0.800000011921 ???????kernel_size: 3 ???????box_code_size: 4 ???????apply_sigmoid_to_scores: false ???????use_depthwise: true ?????} ???} ???anchor_generator { ?????ssd_anchor_generator { ???????num_layers: 6 ???????min_scale: 0.20000000298 ???????max_scale: 0.949999988079 ???????aspect_ratios: 1.0 ???????aspect_ratios: 2.0 ???????aspect_ratios: 0.5 ???????aspect_ratios: 3.0 ???????aspect_ratios: 0.333299994469 ?????} ???} ???post_processing { ?????batch_non_max_suppression { ???????score_threshold: 0.300000011921 ???????iou_threshold: 0.600000023842 ???????max_detections_per_class: 100 ???????max_total_detections: 100 ?????} ?????score_converter: SIGMOID ???} ???normalize_loss_by_num_matches: true ???loss { ?????localization_loss { ???????weighted_smooth_l1 { ???????} ?????} ?????classification_loss { ???????weighted_sigmoid { ???????} ?????} ?????hard_example_miner { ???????num_hard_examples: 3000 ???????iou_threshold: 0.990000009537 ???????loss_type: CLASSIFICATION ???????max_negatives_per_positive: 3 ???????min_negatives_per_image: 3 ?????} ?????classification_weight: 1.0 ?????localization_weight: 1.0 ???} ?}}train_config { ?batch_size: 2 ?data_augmentation_options { ???random_horizontal_flip { ???} ?} ?data_augmentation_options { ???ssd_random_crop { ???} ?} ?optimizer { ???rms_prop_optimizer { ?????learning_rate { ???????exponential_decay_learning_rate { ?????????initial_learning_rate: 0.00400000018999 ?????????decay_steps: 800720 ?????????decay_factor: 0.949999988079 ???????} ?????} ?????momentum_optimizer_value: 0.899999976158 ?????decay: 0.899999976158 ?????epsilon: 1.0 ???} ?} ?fine_tune_checkpoint: "/home/xueaoru/models/research/ssdlite_mobilenet_v2_coco_2018_05_09/model.ckpt" ?num_steps: 200000 ?fine_tune_checkpoint_type: "detection"}train_input_reader { ?label_map_path: "/home/xueaoru/models/research/car_label_map.pbtxt" ?tf_record_input_reader { ???input_path: "/home/xueaoru/models/research/train.record" ?}}eval_config { ?num_examples: 60 ?max_evals: 10 ?use_moving_averages: false}eval_input_reader { ?label_map_path: "/home/xueaoru/models/research/car_label_map.pbtxt" ?shuffle: true ?num_readers: 1 ?tf_record_input_reader { ???input_path: "/home/xueaoru/models/research/test.record" ?}}在models/research目录下执行以下命令：
python object_detection/model_main.py ????--pipeline_config_path=/home/xueaoru/models/research/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config ????--num_train_steps=200000 ????--sample_1_of_n_eval_examples=25 ????--alsologtostderr ????--model_dir=/home/xueaoru/models/research/car_data
其中pipeline_config_path为之前配置好的pipeline的绝对路径
num_train_steps为训练步数
sample_1_of_n_eval_examples为每多少个验证数据抽样一次
alsologtostderr输出std错误信息
model_dir输出训练过程中的数据的存放文件夹
执行完以上命令之后，基本上训练就开始了，我们只需要通过tensorboard来看看训练效果就可以了
tensorboard --logdir car_data
打开输出的地址：
就可以看到训练效果啦
等到差不多收敛了，我们就可以输出我们的模型了
命令行输入以下命令：
python object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path /home/xueaoru/models/research/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config --trained_checkpoint_prefix /home/xueaoru/models/research/car_data/model.ckpt-87564 --output_directory /home/xueaoru/models/research/inference_graph_v2
配置基本上跟上面差不多，改改路径即可。
然后我们就在inference_graph_v2目录下拿到了训练后的模型了。
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原文地址：https://www.cnblogs.com/aoru45/p/9868167.html