最近我在使用TensorFlow对象检测API 2（TF OD 2），我使用的是Git头部提交ce3b7227。我的目标是通过使用TensorFlow 2模型库中现有的深度学习架构，找到最适合我自定义数据集的模型。我使用Roboflow的教程生成了我的TF记录，并在我的笔记本电脑和Google Colab的GPU模式下进行了训练。
我找到了这个很棒的Roboflow的Colab笔记本，当我尝试使用我的数据集重现相同的步骤时，使用了models/research/object_detection/model_main_tf2.py，不幸的是，训练脚本总是在我开始迭代之前就结束了。它没有显示任何Python错误，并且像往常一样显示了一些警告。完整的输出在我的Colab笔记本中
我使用以下命令对模型进行微调。
PIPELINE_CONFIG_PATH=models/ssd_resnet152_v1_fpn_640x640_coco17_tpu-8/pipeline.config; MODEL_DIR=training/; NUM_TRAIN_STEPS=10000; SAMPLE_1_OF_N_EVAL_EXAMPLES=1; python models/research/object_detection/model_main_tf2.py --model_dir=$MODEL_DIR --num_train_steps=$NUM_TRAIN_STEPS --sample_1_of_n_eval_examples=$SAMPLE_1_OF_N_EVAL_EXAMPLES --pipeline_config_path=$PIPELINE_CONFIG_PATH --alsologtostderr这是我的pipeline.config文件
model { ssd { num_classes: 90 image_resizer { fixed_shape_resizer { height: 640 width: 640 } } feature_extractor { type: "ssd_resnet152_v1_fpn_keras" depth_multiplier: 1.0 min_depth: 16 conv_hyperparams { regularizer { l2_regularizer { weight: 0.00039999998989515007 } } initializer { truncated_normal_initializer { mean: 0.0 stddev: 0.029999999329447746 } } activation: RELU_6 batch_norm { decay: 0.996999979019165 scale: true epsilon: 0.0010000000474974513 } } override_base_feature_extractor_hyperparams: true fpn { min_level: 3 max_level: 7 } } 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 use_matmul_gather: true } } similarity_calculator { iou_similarity { } } box_predictor { weight_shared_convolutional_box_predictor { conv_hyperparams { regularizer { l2_regularizer { weight: 0.00039999998989515007 } } initializer { random_normal_initializer { mean: 0.0 stddev: 0.009999999776482582 } } activation: RELU_6 batch_norm { decay: 0.996999979019165 scale: true epsilon: 0.0010000000474974513 } } depth: 256 num_layers_before_predictor: 4 kernel_size: 3 class_prediction_bias_init: -4.599999904632568 } } anchor_generator { multiscale_anchor_generator { min_level: 3 max_level: 7 anchor_scale: 4.0 aspect_ratios: 1.0 aspect_ratios: 2.0 aspect_ratios: 0.5 scales_per_octave: 2 } } post_processing { batch_non_max_suppression { score_threshold: 9.99999993922529e-09 iou_threshold: 0.6000000238418579 max_detections_per_class: 100 max_total_detections: 100 use_static_shapes: false } score_converter: SIGMOID } normalize_loss_by_num_matches: true loss { localization_loss { weighted_smooth_l1 { } } classification_loss { weighted_sigmoid_focal { gamma: 2.0 alpha: 0.25 } } classification_weight: 1.0 localization_weight: 1.0 } encode_background_as_zeros: true normalize_loc_loss_by_codesize: true inplace_batchnorm_update: true freeze_batchnorm: false }}train_config { batch_size: 8 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { random_crop_image { min_object_covered: 0.0 min_aspect_ratio: 0.75 max_aspect_ratio: 3.0 min_area: 0.75 max_area: 1.0 overlap_thresh: 0.0 } } sync_replicas: true optimizer { momentum_optimizer { learning_rate { cosine_decay_learning_rate { learning_rate_base: 0.03999999910593033 total_steps: 25000 warmup_learning_rate: 0.013333000242710114 warmup_steps: 2000 } } momentum_optimizer_value: 0.8999999761581421 } use_moving_average: false } fine_tune_checkpoint_version: V2 fine_tune_checkpoint: "models/ssd_resnet152_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0" num_steps: 25000 startup_delay_steps: 0.0 replicas_to_aggregate: 8 max_number_of_boxes: 100 unpad_groundtruth_tensors: false fine_tune_checkpoint_type: "classification" use_bfloat16: true}train_input_reader { label_map_path: "datasets/UrbanTracker/urban_tracker_label_map.pbtxt" tf_record_input_reader { input_path: "datasets/UrbanTracker/urban_tracker_train.record" }}eval_config { metrics_set: "coco_detection_metrics" use_moving_averages: false}eval_input_reader { label_map_path: "datasets/UrbanTracker/urban_tracker_label_map.pbtxt" shuffle: false num_epochs: 1 tf_record_input_reader { input_path: "datasets/UrbanTracker/urban_tracker_test.record" }}这是我的模型目录的结构。
.├── datasets│ ├── raccoon│ │ ├── raccoon_label_map.pbtxt│ │ ├── raccoon_test.record│ │ └── raccoon_train.record│ ├── readme.md│ └── UrbanTracker│ ├── labels_urbantracker.txt│ ├── urban_tracker_label_map.pbtxt│ ├── urban_tracker_test.record│ └── urban_tracker_train.record├── __main__.py├── models│ ├── AUTHORS│ ├── efficientdet_d1_coco17_tpu-32│ │ ├── checkpoint│ │ │ ├── checkpoint│ │ │ ├── ckpt-0.data-00000-of-00001│ │ │ └── ckpt-0.index│ │ ├── pipeline.config│ │ ├── saved_model│ │ │ ├── assets│ │ │ ├── saved_model.pb│ │ │ └── variables│ │ │ ├── variables.data-00000-of-00001│ │ │ └── variables.index│ ├── faster_rcnn_resnet101_v1_640x640_coco17_tpu-8│ │ ├── checkpoint│ │ │ ├── checkpoint│ │ │ ├── ckpt-0.data-00000-of-00001│ │ │ └── ckpt-0.index│ │ ├── pipeline.config│ │ ├── saved_model│ │ │ ├── saved_model.pb│ │ │ └── variables│ │ │ ├── variables.data-00000-of-00001│ │ │ └── variables.index│ └── ssd_resnet152_v1_fpn_640x640_coco17_tpu-8│ ├── checkpoint│ │ ├── checkpoint│ │ ├── ckpt-0.data-00000-of-00001│ │ └── ckpt-0.index│ ├── pipeline.config│ ├── saved_model│ │ ├── assets│ │ ├── saved_model.pb│ │ └── variables│ │ ├── variables.data-00000-of-00001│ │ └── variables.index├── tools│ ├── parse_polytrack.py│ ├── polytrack_csv_to_tfrecord.py│ ├── raccoon_labels_test.csv│ ├── raccoon_labels_train.csv│ ├── split_dataset.py│ ├── urban_tracker_test.csv│ └── urban_tracker_train.csv我已经使用TF v1和v2 API将我的数据集转换为TFRecord。此外，我尝试了不同的训练参数但没有成功。为了检查我的数据集是否生成错误，我尝试了另一个数据集，即基本的Raccoon数据集，但得到了相同的结果。
感谢您的关注。
回答：
解决方案：对于像efficientdet_d1_coco17_tpu-32这样的模型，只需在pipeline.config文件中将参数从fine_tune_checkpoint_type: "classification"更改为fine_tune_checkpoint_type: "detection"，请查看TF GitHub
