In the previous blogs we have seen how to generate data for object detection and convert it into TFRecord format to train the model. In this blog we will learn how to use this data to train the model.
To train the model we will use the pre-trained model and then use transfer learning to train it on our dataset. I have used
The configuration file that we have downloaded, needs to be edited as per our requirement. In configuration
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# SSD with Mobilenet v1, configured for Oxford-IIIT Pets Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that # should be configured. model { ssd { num_classes: 4 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_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 { classification_loss { weighted_sigmoid { anchorwise_output: true } } localization_loss { weighted_smooth_l1 { anchorwise_output: true } } 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: 16 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: "D:/models/research/images/ssd_mobilenet_v1_coco_11_06_2017/model.ckpt" from_detection_checkpoint: true # Note: The below line limits the training process to 200K steps, which we # empirically found to be sufficient enough to train the pets dataset. This # effectively bypasses the learning rate schedule (the learning rate will # never decay). Remove the below line to train indefinitely. num_steps: 200000 data_augmentation_options { random_horizontal_flip { } } data_augmentation_options { ssd_random_crop { } } } train_input_reader: { tf_record_input_reader { input_path: "D:/models/research/images/data/train.record" } label_map_path: "D:/models/research/images/data/object-detection.pbtxt" } eval_config: { num_examples: 2000 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. max_evals: 10 } eval_input_reader: { tf_record_input_reader { input_path: "D:/models/research/images/data/test.record" } label_map_path: "D:/models/research/images/data/object-detection.pbtxt" shuffle: false num_readers: 1 } |
For the object-detection.pbtxt file,
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item { id: 1 name: 'up' } item { id:2 name:"down" } item { id:3 name:"left" } item { id:4 name:"right" } |
Now go to models -> research -> object detection -> legecy and copy train.py file to models -> research folder.
Then create a folder named images inside models -> research folder. Put your mobilenet model, configuration file, train and test image data folders, and train and test csv label files. Inside training_data folder,
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images -data -object-detection.pbtxt -test.record -train.record -test -contains all test images data -train -contains all train images data -ssd_mobilenet_v1_pets.config -test_labels.csv -train_labels.csv -training |
Also create a training folder inside the images folder where model will save its checkpoints. Now run the following command to train the model from models -> research folder.
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python train.py --logtostderr --train_dir=images/training/ --pipeline_config_path=images/ssd_mobilenet_v1_pets.config |
Time for training your model will depend upon your machine configuration and no. of steps that you have mentioned in the configuration file.
Now we have our trained model and its checkpoints are saved inside the models/research/images/training folder. In order to test this model and use this model to detect objects we need to export the inference graph.
To do this first we need to copy models/research/object_detection/export_inference_graph.py to models/research/ folder. Then inside models/research folder create a folder named “snake” which will save the inference graph. From models -> research folder run the following command:
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python export_inference_graph.py --input_type image_tensor --pipeline_config_path images/ssd_mobilenet_v1_pets.config --trained_checkpoint_prefix images/training/model.ckpt-34345 --output_directory snake |
Now we are having forzen_inference_graph.pb inside models/research/snake folder which will be used to detect object using trained model.
This is all for training the model and saving the inference graph, in the next blog we will see how to use this inference graph for object detection and how to run our snake game using this trained object detection model.
Next Blog: Snake Game Using Tensorflow Object Detection API – Part IV
Hope you enjoy reading.
If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.