package object_detection.protos
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message
optimizer.proto:37Configuration message for the AdamOptimizer See: https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer
Used in:
optional learning_rate = 1
message
anchor_generator.proto:10Configuration proto for the anchor generator to use in the object detection pipeline. See core/anchor_generator.py for details.
Used in: ,
oneof anchor_generator_oneof
grid_anchor_generator = 1
ssd_anchor_generator = 2
message
argmax_matcher.proto:7Configuration proto for ArgMaxMatcher. See matchers/argmax_matcher.py for details.
Used in:
optional float matched_threshold = 1
Threshold for positive matches.
optional float unmatched_threshold = 2
Threshold for negative matches.
optional bool ignore_thresholds = 3
Whether to construct ArgMaxMatcher without thresholds.
optional bool negatives_lower_than_unmatched = 4
If True then negative matches are the ones below the unmatched_threshold, whereas ignored matches are in between the matched and umatched threshold. If False, then negative matches are in between the matched and unmatched threshold, and everything lower than unmatched is ignored.
optional bool force_match_for_each_row = 5
Whether to ensure each row is matched to at least one column.
message
post_processing.proto:7Configuration proto for non-max-suppression operation on a batch of detections.
Used in:
optional float score_threshold = 1
Scalar threshold for score (low scoring boxes are removed).
optional float iou_threshold = 2
Scalar threshold for IOU (boxes that have high IOU overlap with previously selected boxes are removed).
optional int32 max_detections_per_class = 3
Maximum number of detections to retain per class.
optional int32 max_total_detections = 5
Maximum number of detections to retain across all classes.
message
hyperparams.proto:94Configuration proto for batch norm to apply after convolution op. See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/batch_norm
Used in:
optional float decay = 1
optional bool center = 2
optional bool scale = 3
optional float epsilon = 4
optional bool train = 5
Whether to train the batch norm variables. If this is set to false during training, the current value of the batch_norm variables are used for forward pass but they are never updated.
message
bipartite_matcher.proto:7Configuration proto for bipartite matcher. See matchers/bipartite_matcher.py for details.
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(message has no fields)
message
losses.proto:87Classification loss using a sigmoid function over the class prediction with the highest prediction score.
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optional float alpha = 1
Interpolation weight between 0 and 1.
optional bool hard_bootstrap = 2
Whether hard boot strapping should be used or not. If true, will only use one class favored by model. Othewise, will use all predicted class probabilities.
optional bool anchorwise_output = 3
Output loss per anchor.
message
box_coder.proto:12Configuration proto for the box coder to be used in the object detection pipeline. See core/box_coder.py for details.
Used in:
oneof box_coder_oneof
faster_rcnn_box_coder = 1
mean_stddev_box_coder = 2
square_box_coder = 3
keypoint_box_coder = 4
message
box_predictor.proto:9Configuration proto for box predictor. See core/box_predictor.py for details.
Used in: ,
oneof box_predictor_oneof
convolutional_box_predictor = 1
mask_rcnn_box_predictor = 2
rfcn_box_predictor = 3
message
losses.proto:51Configuration for class prediction loss function.
Used in: ,
oneof classification_loss
weighted_sigmoid = 1
weighted_softmax = 2
bootstrapped_sigmoid = 3
weighted_sigmoid_focal = 4
message
optimizer.proto:52Configuration message for a constant learning rate.
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optional float learning_rate = 1
message
box_predictor.proto:18Configuration proto for Convolutional box predictor.
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optional conv_hyperparams = 1
Hyperparameters for convolution ops used in the box predictor.
optional int32 min_depth = 2
Minumum feature depth prior to predicting box encodings and class predictions.
optional int32 max_depth = 3
Maximum feature depth prior to predicting box encodings and class predictions. If max_depth is set to 0, no additional feature map will be inserted before location and class predictions.
optional int32 num_layers_before_predictor = 4
Number of the additional conv layers before the predictor.
optional bool use_dropout = 5
Whether to use dropout for class prediction.
optional float dropout_keep_probability = 6
Keep probability for dropout
optional int32 kernel_size = 7
Size of final convolution kernel. If the spatial resolution of the feature map is smaller than the kernel size, then the kernel size is set to min(feature_width, feature_height).
optional int32 box_code_size = 8
Size of the encoding for boxes.
optional bool apply_sigmoid_to_scores = 9
Whether to apply sigmoid to the output of class predictions. TODO: Do we need this since we have a post processing module.?
optional float class_prediction_bias_init = 10
message
optimizer.proto:78Configuration message for a cosine decaying learning rate as defined in object_detection/utils/learning_schedules.py
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optional float learning_rate_base = 1
optional uint32 total_steps = 2
optional float warmup_learning_rate = 3
optional uint32 warmup_steps = 4
message
model.proto:9Top level configuration for DetectionModels.
Used in:
oneof model
faster_rcnn = 1
ssd = 2
message
eval.proto:6Message for configuring DetectionModel evaluation jobs (eval.py).
Used in:
optional uint32 num_visualizations = 1
Number of visualization images to generate.
optional uint32 num_examples = 2
Number of examples to process of evaluation.
optional uint32 eval_interval_secs = 3
How often to run evaluation.
optional uint32 max_evals = 4
Maximum number of times to run evaluation. If set to 0, will run forever.
optional bool save_graph = 5
Whether the TensorFlow graph used for evaluation should be saved to disk.
optional string visualization_export_dir = 6
Path to directory to store visualizations in. If empty, visualization images are not exported (only shown on Tensorboard).
optional string eval_master = 7
BNS name of the TensorFlow master.
optional string metrics_set = 8
Type of metrics to use for evaluation. Currently supports only Pascal VOC detection metrics.
optional string export_path = 9
Path to export detections to COCO compatible JSON format.
optional bool ignore_groundtruth = 10
Option to not read groundtruth labels and only export detections to COCO-compatible JSON file.
optional bool use_moving_averages = 11
Use exponential moving averages of variables for evaluation.
optional bool eval_instance_masks = 12
Whether to evaluate instance masks. Note that since there is no evaluation code currently for instance segmenation this option is unused.
message
optimizer.proto:59Configuration message for an exponentially decaying learning rate. See https://www.tensorflow.org/versions/master/api_docs/python/train/ \ decaying_the_learning_rate#exponential_decay
Used in:
optional float initial_learning_rate = 1
optional uint32 decay_steps = 2
optional float decay_factor = 3
optional bool staircase = 4
message
input_reader.proto:58An externally defined input reader. Users may define an extension to this proto to interface their own input readers.
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(message has no fields)
message
faster_rcnn.proto:20Configuration for Faster R-CNN models. See meta_architectures/faster_rcnn_meta_arch.py and models/model_builder.py Naming conventions: Faster R-CNN models have two stages: a first stage region proposal network (or RPN) and a second stage box classifier. We thus use the prefixes `first_stage_` and `second_stage_` to indicate the stage to which each parameter pertains when relevant.
Used in:
optional bool first_stage_only = 1
Whether to construct only the Region Proposal Network (RPN).
optional int32 num_classes = 3
Number of classes to predict.
optional image_resizer = 4
Image resizer for preprocessing the input image.
optional feature_extractor = 5
Feature extractor config.
optional first_stage_anchor_generator = 6
Anchor generator to compute RPN anchors.
optional int32 first_stage_atrous_rate = 7
Atrous rate for the convolution op applied to the `first_stage_features_to_crop` tensor to obtain box predictions.
optional first_stage_box_predictor_conv_hyperparams = 8
Hyperparameters for the convolutional RPN box predictor.
optional int32 first_stage_box_predictor_kernel_size = 9
Kernel size to use for the convolution op just prior to RPN box predictions.
optional int32 first_stage_box_predictor_depth = 10
Output depth for the convolution op just prior to RPN box predictions.
optional int32 first_stage_minibatch_size = 11
The batch size to use for computing the first stage objectness and location losses.
optional float first_stage_positive_balance_fraction = 12
Fraction of positive examples per image for the RPN.
optional float first_stage_nms_score_threshold = 13
Non max suppression score threshold applied to first stage RPN proposals.
optional float first_stage_nms_iou_threshold = 14
Non max suppression IOU threshold applied to first stage RPN proposals.
optional int32 first_stage_max_proposals = 15
Maximum number of RPN proposals retained after first stage postprocessing.
optional float first_stage_localization_loss_weight = 16
First stage RPN localization loss weight.
optional float first_stage_objectness_loss_weight = 17
First stage RPN objectness loss weight.
optional int32 initial_crop_size = 18
Output size (width and height are set to be the same) of the initial bilinear interpolation based cropping during ROI pooling.
optional int32 maxpool_kernel_size = 19
Kernel size of the max pool op on the cropped feature map during ROI pooling.
optional int32 maxpool_stride = 20
Stride of the max pool op on the cropped feature map during ROI pooling.
optional second_stage_box_predictor = 21
Hyperparameters for the second stage box predictor. If box predictor type is set to rfcn_box_predictor, a R-FCN model is constructed, otherwise a Faster R-CNN model is constructed.
optional int32 second_stage_batch_size = 22
The batch size per image used for computing the classification and refined location loss of the box classifier. Note that this field is ignored if `hard_example_miner` is configured.
optional float second_stage_balance_fraction = 23
Fraction of positive examples to use per image for the box classifier.
optional second_stage_post_processing = 24
Post processing to apply on the second stage box classifier predictions. Note: the `score_converter` provided to the FasterRCNNMetaArch constructor is taken from this `second_stage_post_processing` proto.
optional float second_stage_localization_loss_weight = 25
Second stage refined localization loss weight.
optional float second_stage_classification_loss_weight = 26
Second stage classification loss weight
optional float second_stage_mask_prediction_loss_weight = 27
Second stage instance mask loss weight. Note that this is only applicable when `MaskRCNNBoxPredictor` is selected for second stage and configured to predict instance masks.
optional hard_example_miner = 28
If not left to default, applies hard example mining only to classification and localization loss..
optional second_stage_classification_loss = 29
Loss for second stage box classifers, supports Softmax and Sigmoid. Note that score converter must be consistent with loss type. When there are multiple labels assigned to the same boxes, recommend to use sigmoid loss and enable merge_multiple_label_boxes. If not specified, Softmax loss is used as default.
message
faster_rcnn_box_coder.proto:7Configuration proto for FasterRCNNBoxCoder. See box_coders/faster_rcnn_box_coder.py for details.
Used in:
optional float y_scale = 1
Scale factor for anchor encoded box center.
optional float x_scale = 2
optional float height_scale = 3
Scale factor for anchor encoded box height.
optional float width_scale = 4
Scale factor for anchor encoded box width.
message
faster_rcnn.proto:137Used in:
optional string type = 1
Type of Faster R-CNN model (e.g., 'faster_rcnn_resnet101'; See builders/model_builder.py for expected types).
optional int32 first_stage_features_stride = 2
Output stride of extracted RPN feature map.
optional bool batch_norm_trainable = 3
Whether to update batch norm parameters during training or not. When training with a relative large batch size (e.g. 8), it could be desirable to enable batch norm update.
message
image_resizer.proto:35Configuration proto for image resizer that resizes to a fixed shape.
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optional int32 height = 1
Desired height of image in pixels.
optional int32 width = 2
Desired width of image in pixels.
optional ResizeType resize_method = 3
Desired method when resizing image.
message
grid_anchor_generator.proto:7Configuration proto for GridAnchorGenerator. See anchor_generators/grid_anchor_generator.py for details.
Used in:
optional int32 height = 1
Anchor height in pixels.
optional int32 width = 2
Anchor width in pixels.
optional int32 height_stride = 3
Anchor stride in height dimension in pixels.
optional int32 width_stride = 4
Anchor stride in width dimension in pixels.
optional int32 height_offset = 5
Anchor height offset in pixels.
optional int32 width_offset = 6
Anchor width offset in pixels.
repeated float scales = 7
List of scales for the anchors.
repeated float aspect_ratios = 8
List of aspect ratios for the anchors.
message
losses.proto:101Configuation for hard example miner.
Used in: ,
optional int32 num_hard_examples = 1
Maximum number of hard examples to be selected per image (prior to enforcing max negative to positive ratio constraint). If set to 0, all examples obtained after NMS are considered.
optional float iou_threshold = 2
Minimum intersection over union for an example to be discarded during NMS.
optional HardExampleMiner.LossType loss_type = 3
optional int32 max_negatives_per_positive = 4
Maximum number of negatives to retain for each positive anchor. If num_negatives_per_positive is 0 no prespecified negative:positive ratio is enforced.
optional int32 min_negatives_per_image = 5
Minimum number of negative anchors to sample for a given image. Setting this to a positive number samples negatives in an image without any positive anchors and thus not bias the model towards having at least one detection per image.
Whether to use classification losses ('cls', default), localization losses ('loc') or both losses ('both'). In the case of 'both', cls_loss_weight and loc_loss_weight are used to compute weighted sum of the two losses.
Used in:
BOTH = 0
CLASSIFICATION = 1
LOCALIZATION = 2
message
hyperparams.proto:7Configuration proto for the convolution op hyperparameters to use in the object detection pipeline.
Used in: , , , ,
optional Hyperparams.Op op = 1
optional regularizer = 2
Regularizer for the weights of the convolution op.
optional initializer = 3
Initializer for the weights of the convolution op.
optional Hyperparams.Activation activation = 4
optional batch_norm = 5
BatchNorm hyperparameters. If this parameter is NOT set then BatchNorm is not applied!
Type of activation to apply after convolution.
Used in:
NONE = 0
Use None (no activation)
RELU = 1
Use tf.nn.relu
RELU_6 = 2
Use tf.nn.relu6
Operations affected by hyperparameters.
Used in:
CONV = 1
Convolution, Separable Convolution, Convolution transpose.
FC = 2
Fully connected
message
image_resizer.proto:7Configuration proto for image resizing operations. See builders/image_resizer_builder.py for details.
Used in: ,
oneof image_resizer_oneof
keep_aspect_ratio_resizer = 1
fixed_shape_resizer = 2
message
hyperparams.proto:64Proto with one-of field for initializers.
Used in:
oneof initializer_oneof
truncated_normal_initializer = 1
variance_scaling_initializer = 2
message
input_reader.proto:18Used in:
optional string label_map_path = 1
Path to StringIntLabelMap pbtxt file specifying the mapping from string labels to integer ids.
optional bool shuffle = 2
Whether data should be processed in the order they are read in, or shuffled randomly.
optional uint32 queue_capacity = 3
Maximum number of records to keep in reader queue.
optional uint32 min_after_dequeue = 4
Minimum number of records to keep in reader queue. A large value is needed to generate a good random shuffle.
optional uint32 num_epochs = 5
The number of times a data source is read. If set to zero, the data source will be reused indefinitely.
optional uint32 num_readers = 6
Number of reader instances to create.
optional bool load_instance_masks = 7
Whether to load groundtruth instance masks.
oneof input_reader
tf_record_input_reader = 8
external_input_reader = 9
Configuration for intersection-over-area (IOA) similarity calculator.
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(message has no fields)
Configuration for intersection-over-union (IOU) similarity calculator.
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(message has no fields)
message
image_resizer.proto:23Configuration proto for image resizer that keeps aspect ratio.
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optional int32 min_dimension = 1
Desired size of the smaller image dimension in pixels.
optional int32 max_dimension = 2
Desired size of the larger image dimension in pixels.
optional ResizeType resize_method = 3
Desired method when resizing image.
message
keypoint_box_coder.proto:7Configuration proto for KeypointBoxCoder. See box_coders/keypoint_box_coder.py for details.
Used in:
optional int32 num_keypoints = 1
optional float y_scale = 2
Scale factor for anchor encoded box center and keypoints.
optional float x_scale = 3
optional float height_scale = 4
Scale factor for anchor encoded box height.
optional float width_scale = 5
Scale factor for anchor encoded box width.
message
hyperparams.proto:53Configuration proto for L1 Regularizer. See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/l1_regularizer
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optional float weight = 1
message
hyperparams.proto:59Configuration proto for L2 Regularizer. See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/l2_regularizer
Used in:
optional float weight = 1
message
optimizer.proto:42Configuration message for optimizer learning rate.
Used in: , ,
oneof learning_rate
constant_learning_rate = 1
exponential_decay_learning_rate = 2
manual_step_learning_rate = 3
cosine_decay_learning_rate = 4
message
losses.proto:26Configuration for bounding box localization loss function.
Used in:
oneof localization_loss
weighted_l2 = 1
weighted_smooth_l1 = 2
weighted_iou = 3
message
losses.proto:8Message for configuring the localization loss, classification loss and hard example miner used for training object detection models. See core/losses.py for details
Used in:
optional localization_loss = 1
Localization loss to use.
optional classification_loss = 2
Classification loss to use.
optional hard_example_miner = 3
If not left to default, applies hard example mining.
optional float classification_weight = 4
Classification loss weight.
optional float localization_weight = 5
Localization loss weight.
message
optimizer.proto:67Configuration message for a manually defined learning rate schedule.
Used in:
optional float initial_learning_rate = 1
repeated schedule = 2
message
optimizer.proto:69Used in:
optional uint32 step = 1
optional float learning_rate = 2
message
box_predictor.proto:55Used in:
optional fc_hyperparams = 1
Hyperparameters for fully connected ops used in the box predictor.
optional bool use_dropout = 2
Whether to use dropout op prior to the both box and class predictions.
optional float dropout_keep_probability = 3
Keep probability for dropout. This is only used if use_dropout is true.
optional int32 box_code_size = 4
Size of the encoding for the boxes.
optional conv_hyperparams = 5
Hyperparameters for convolution ops used in the box predictor.
optional bool predict_instance_masks = 6
Whether to predict instance masks inside detection boxes.
optional int32 mask_prediction_conv_depth = 7
The depth for the first conv2d_transpose op applied to the image_features in the mask prediciton branch
optional bool predict_keypoints = 8
Whether to predict keypoints inside detection boxes.
message
matcher.proto:10Configuration proto for the matcher to be used in the object detection pipeline. See core/matcher.py for details.
Used in:
oneof matcher_oneof
argmax_matcher = 1
bipartite_matcher = 2
message
mean_stddev_box_coder.proto:7Configuration proto for MeanStddevBoxCoder. See box_coders/mean_stddev_box_coder.py for details.
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(message has no fields)
message
optimizer.proto:30Configuration message for the MomentumOptimizer See: https://www.tensorflow.org/api_docs/python/tf/train/MomentumOptimizer
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optional learning_rate = 1
optional float momentum_optimizer_value = 2
Configuration for negative squared distance similarity calculator.
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(message has no fields)
message
preprocessor.proto:41Normalizes pixel values in an image. For every channel in the image, moves the pixel values from the range [original_minval, original_maxval] to [target_minval, target_maxval].
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optional float original_minval = 1
optional float original_maxval = 2
optional float target_minval = 3
optional float target_maxval = 4
message
optimizer.proto:9Top level optimizer message.
Used in:
oneof optimizer
rms_prop_optimizer = 1
momentum_optimizer = 2
adam_optimizer = 3
optional bool use_moving_average = 4
optional float moving_average_decay = 5
message
post_processing.proto:24Configuration proto for post-processing predicted boxes and scores.
Used in: ,
optional batch_non_max_suppression = 1
Non max suppression parameters.
optional PostProcessing.ScoreConverter score_converter = 2
Score converter to use.
optional float logit_scale = 3
Scale logit (input) value before conversion in post-processing step. Typically used for softmax distillation, though can be used to scale for other reasons.
Enum to specify how to convert the detection scores.
Used in:
IDENTITY = 0
Input scores equals output scores.
SIGMOID = 1
Applies a sigmoid on input scores.
SOFTMAX = 2
Applies a softmax on input scores
message
preprocessor.proto:7Message for defining a preprocessing operation on input data. See: //object_detection/core/preprocessor.py
Used in:
oneof preprocessing_step
normalize_image = 1
random_horizontal_flip = 2
random_pixel_value_scale = 3
random_image_scale = 4
random_rgb_to_gray = 5
random_adjust_brightness = 6
random_adjust_contrast = 7
random_adjust_hue = 8
random_adjust_saturation = 9
random_distort_color = 10
random_jitter_boxes = 11
random_crop_image = 12
random_pad_image = 13
random_crop_pad_image = 14
random_crop_to_aspect_ratio = 15
random_black_patches = 16
random_resize_method = 17
scale_boxes_to_pixel_coordinates = 18
resize_image = 19
subtract_channel_mean = 20
ssd_random_crop = 21
ssd_random_crop_pad = 22
ssd_random_crop_fixed_aspect_ratio = 23
ssd_random_crop_pad_fixed_aspect_ratio = 24
random_vertical_flip = 25
random_rotation90 = 26
message
optimizer.proto:21Configuration message for the RMSPropOptimizer See: https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer
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optional learning_rate = 1
optional float momentum_optimizer_value = 2
optional float decay = 3
optional float epsilon = 4
message
preprocessor.proto:106Randomly changes image brightness by up to max_delta. Image outputs will be saturated between 0 and 1.
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optional float max_delta = 1
message
preprocessor.proto:111Randomly scales contract by a value between [min_delta, max_delta].
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optional float min_delta = 1
optional float max_delta = 2
message
preprocessor.proto:117Randomly alters hue by a value of up to max_delta.
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optional float max_delta = 1
message
preprocessor.proto:122Randomly changes saturation by a value between [min_delta, max_delta].
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optional float min_delta = 1
optional float max_delta = 2
message
preprocessor.proto:221Randomly adds black square patches to an image.
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optional int32 max_black_patches = 1
The maximum number of black patches to add.
optional float probability = 2
The probability of a black patch being added to an image.
optional float size_to_image_ratio = 3
Ratio between the dimension of the black patch to the minimum dimension of the image (patch_width = patch_height = min(image_height, image_width)).
message
preprocessor.proto:139Randomly crops the image and bounding boxes.
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optional float min_object_covered = 1
Cropped image must cover at least one box by this fraction.
optional float min_aspect_ratio = 2
Aspect ratio bounds of cropped image.
optional float max_aspect_ratio = 3
optional float min_area = 4
Allowed area ratio of cropped image to original image.
optional float max_area = 5
optional float overlap_thresh = 6
Minimum overlap threshold of cropped boxes to keep in new image. If the ratio between a cropped bounding box and the original is less than this value, it is removed from the new image.
optional float random_coef = 7
Probability of keeping the original image.
message
preprocessor.proto:178Randomly crops an image followed by a random pad.
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optional float min_object_covered = 1
Cropping operation must cover at least one box by this fraction.
optional float min_aspect_ratio = 2
Aspect ratio bounds of image after cropping operation.
optional float max_aspect_ratio = 3
optional float min_area = 4
Allowed area ratio of image after cropping operation.
optional float max_area = 5
optional float overlap_thresh = 6
Minimum overlap threshold of cropped boxes to keep in new image. If the ratio between a cropped bounding box and the original is less than this value, it is removed from the new image.
optional float random_coef = 7
Probability of keeping the original image during the crop operation.
repeated float min_padded_size_ratio = 8
Maximum dimensions for padded image. If unset, will use double the original image dimension as a lower bound. Both of the following fields should be length 2.
repeated float max_padded_size_ratio = 9
repeated float pad_color = 10
Color of the padding. If unset, will pad using average color of the input image.
message
preprocessor.proto:210Randomly crops an iamge to a given aspect ratio.
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optional float aspect_ratio = 1
Aspect ratio.
optional float overlap_thresh = 2
Minimum overlap threshold of cropped boxes to keep in new image. If the ratio between a cropped bounding box and the original is less than this value, it is removed from the new image.
message
preprocessor.proto:128Performs a random color distortion. color_orderings should either be 0 or 1.
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optional int32 color_ordering = 1
message
preprocessor.proto:49Randomly horizontally flips the image and detections 50% of the time.
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repeated int32 keypoint_flip_permutation = 1
Specifies a mapping from the original keypoint indices to horizontally flipped indices. This is used in the event that keypoints are specified, in which case when the image is horizontally flipped the keypoints will need to be permuted. E.g. for keypoints representing left_eye, right_eye, nose_tip, mouth, left_ear, right_ear (in that order), one might specify the keypoint_flip_permutation below: keypoint_flip_permutation: 1 keypoint_flip_permutation: 0 keypoint_flip_permutation: 2 keypoint_flip_permutation: 3 keypoint_flip_permutation: 5 keypoint_flip_permutation: 4
message
preprocessor.proto:94Randomly enlarges or shrinks image (keeping aspect ratio).
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optional float min_scale_ratio = 1
optional float max_scale_ratio = 2
message
preprocessor.proto:134Randomly jitters corners of boxes in the image determined by ratio. ie. If a box is [100, 200] and ratio is 0.02, the corners can move by [1, 4].
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optional float ratio = 1
message
preprocessor.proto:161Randomly adds padding to the image.
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optional float min_image_height = 1
Minimum dimensions for padded image. If unset, will use original image dimension as a lower bound.
optional float min_image_width = 2
optional float max_image_height = 3
Maximum dimensions for padded image. If unset, will use double the original image dimension as a lower bound.
optional float max_image_width = 4
repeated float pad_color = 5
Color of the padding. If unset, will pad using average color of the input image.
message
preprocessor.proto:88Randomly scales the values of all pixels in the image by some constant value between [minval, maxval], then clip the value to a range between [0, 1.0].
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optional float minval = 1
optional float maxval = 2
message
preprocessor.proto:100Randomly convert entire image to grey scale.
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optional float probability = 1
message
preprocessor.proto:234Randomly resizes the image up to [target_height, target_width].
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optional float target_height = 1
optional float target_width = 2
message
preprocessor.proto:84Randomly rotates the image and detections by 90 degrees counter-clockwise 50% of the time.
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(message has no fields)
message
preprocessor.proto:66Randomly vertically flips the image and detections 50% of the time.
Used in:
repeated int32 keypoint_flip_permutation = 1
Specifies a mapping from the original keypoint indices to vertically flipped indices. This is used in the event that keypoints are specified, in which case when the image is vertically flipped the keypoints will need to be permuted. E.g. for keypoints representing left_eye, right_eye, nose_tip, mouth, left_ear, right_ear (in that order), one might specify the keypoint_flip_permutation below: keypoint_flip_permutation: 1 keypoint_flip_permutation: 0 keypoint_flip_permutation: 2 keypoint_flip_permutation: 3 keypoint_flip_permutation: 5 keypoint_flip_permutation: 4
Configuration proto for region similarity calculators. See core/region_similarity_calculator.py for details.
Used in:
oneof region_similarity
neg_sq_dist_similarity = 1
iou_similarity = 2
ioa_similarity = 3
message
hyperparams.proto:44Proto with one-of field for regularizers.
Used in:
oneof regularizer_oneof
l1_regularizer = 1
l2_regularizer = 2
message
preprocessor.proto:244Resizes images to [new_height, new_width].
Used in:
optional int32 new_height = 1
optional int32 new_width = 2
optional ResizeImage.Method method = 3
Used in:
AREA = 1
BICUBIC = 2
BILINEAR = 3
NEAREST_NEIGHBOR = 4
Enumeration type for image resizing methods provided in TensorFlow.
Used in: ,
BILINEAR = 0
Corresponds to tf.image.ResizeMethod.BILINEAR
NEAREST_NEIGHBOR = 1
Corresponds to tf.image.ResizeMethod.NEAREST_NEIGHBOR
BICUBIC = 2
Corresponds to tf.image.ResizeMethod.BICUBIC
AREA = 3
Corresponds to tf.image.ResizeMethod.AREA
message
box_predictor.proto:82Used in:
optional conv_hyperparams = 1
Hyperparameters for convolution ops used in the box predictor.
optional int32 num_spatial_bins_height = 2
Bin sizes for RFCN crops.
optional int32 num_spatial_bins_width = 3
optional int32 depth = 4
Target depth to reduce the input image features to.
optional int32 box_code_size = 5
Size of the encoding for the boxes.
optional int32 crop_height = 6
Size to resize the rfcn crops to.
optional int32 crop_width = 7
message
preprocessor.proto:289Randomly crops a image according to: Liu et al., SSD: Single shot multibox detector. This preprocessing step defines multiple SSDRandomCropOperations. Only one operation (chosen at random) is actually performed on an image.
Used in:
repeated operations = 1
message
preprocessor.proto:358Randomly crops a image to a fixed aspect ratio according to: Liu et al., SSD: Single shot multibox detector. Multiple SSDRandomCropFixedAspectRatioOperations are defined by this preprocessing step. Only one operation (chosen at random) is actually performed on an image.
Used in:
repeated operations = 1
optional float aspect_ratio = 2
Aspect ratio to crop to. This value is used for all crop operations.
message
preprocessor.proto:336Used in:
optional float min_object_covered = 1
Cropped image must cover at least this fraction of one original bounding box.
optional float min_area = 4
The area of the cropped image must be within the range of [min_area, max_area].
optional float max_area = 5
optional float overlap_thresh = 6
Cropped box area ratio must be above this threhold to be kept.
optional float random_coef = 7
Probability a crop operation is skipped.
message
preprocessor.proto:263Used in:
optional float min_object_covered = 1
Cropped image must cover at least this fraction of one original bounding box.
optional float min_aspect_ratio = 2
The aspect ratio of the cropped image must be within the range of [min_aspect_ratio, max_aspect_ratio].
optional float max_aspect_ratio = 3
optional float min_area = 4
The area of the cropped image must be within the range of [min_area, max_area].
optional float max_area = 5
optional float overlap_thresh = 6
Cropped box area ratio must be above this threhold to be kept.
optional float random_coef = 7
Probability a crop operation is skipped.
message
preprocessor.proto:332Randomly crops and pads an image according to: Liu et al., SSD: Single shot multibox detector. This preprocessing step defines multiple SSDRandomCropPadOperations. Only one operation (chosen at random) is actually performed on an image.
Used in:
repeated operations = 1
message
preprocessor.proto:400Randomly crops and pads an image to a fixed aspect ratio according to: Liu et al., SSD: Single shot multibox detector. Multiple SSDRandomCropPadFixedAspectRatioOperations are defined by this preprocessing step. Only one operation (chosen at random) is actually performed on an image.
Used in:
repeated operations = 1
optional float aspect_ratio = 2
Aspect ratio to pad to. This value is used for all crop and pad operations.
message
preprocessor.proto:365Used in:
optional float min_object_covered = 1
Cropped image must cover at least this fraction of one original bounding box.
optional float min_aspect_ratio = 2
The aspect ratio of the cropped image must be within the range of [min_aspect_ratio, max_aspect_ratio].
optional float max_aspect_ratio = 3
optional float min_area = 4
The area of the cropped image must be within the range of [min_area, max_area].
optional float max_area = 5
optional float overlap_thresh = 6
Cropped box area ratio must be above this threhold to be kept.
optional float random_coef = 7
Probability a crop operation is skipped.
repeated float min_padded_size_ratio = 8
Min ratio of padded image height and width to the input image's height and width. Two entries per operation.
repeated float max_padded_size_ratio = 9
Max ratio of padded image height and width to the input image's height and width. Two entries per operation.
message
preprocessor.proto:293Used in:
optional float min_object_covered = 1
Cropped image must cover at least this fraction of one original bounding box.
optional float min_aspect_ratio = 2
The aspect ratio of the cropped image must be within the range of [min_aspect_ratio, max_aspect_ratio].
optional float max_aspect_ratio = 3
optional float min_area = 4
The area of the cropped image must be within the range of [min_area, max_area].
optional float max_area = 5
optional float overlap_thresh = 6
Cropped box area ratio must be above this threhold to be kept.
optional float random_coef = 7
Probability a crop operation is skipped.
repeated float min_padded_size_ratio = 8
Min ratio of padded image height and width to the input image's height and width. Two entries per operation.
repeated float max_padded_size_ratio = 9
Max ratio of padded image height and width to the input image's height and width. Two entries per operation.
optional float pad_color_r = 10
Padding color.
optional float pad_color_g = 11
optional float pad_color_b = 12
message
preprocessor.proto:240Scales boxes from normalized coordinates to pixel coordinates.
Used in:
(message has no fields)
message
losses.proto:68Sigmoid Focal cross entropy loss as described in https://arxiv.org/abs/1708.02002
Used in:
optional bool anchorwise_output = 1
optional float gamma = 2
modulating factor for the loss.
optional float alpha = 3
alpha weighting factor for the loss.
message
square_box_coder.proto:7Configuration proto for SquareBoxCoder. See box_coders/square_box_coder.py for details.
Used in:
optional float y_scale = 1
Scale factor for anchor encoded box center.
optional float x_scale = 2
optional float length_scale = 3
Scale factor for anchor encoded box length.
message
ssd.proto:15Configuration for Single Shot Detection (SSD) models.
Used in:
optional int32 num_classes = 1
Number of classes to predict.
optional image_resizer = 2
Image resizer for preprocessing the input image.
optional feature_extractor = 3
Feature extractor config.
optional box_coder = 4
Box coder to encode the boxes.
optional matcher = 5
Matcher to match groundtruth with anchors.
optional similarity_calculator = 6
Region similarity calculator to compute similarity of boxes.
optional box_predictor = 7
Box predictor to attach to the features.
optional anchor_generator = 8
Anchor generator to compute anchors.
optional post_processing = 9
Post processing to apply on the predictions.
optional bool normalize_loss_by_num_matches = 10
Whether to normalize the loss by number of groundtruth boxes that match to the anchors.
optional loss = 11
Loss configuration for training.
message
ssd_anchor_generator.proto:8Configuration proto for SSD anchor generator described in https://arxiv.org/abs/1512.02325. See anchor_generators/multiple_grid_anchor_generator.py for details.
Used in:
optional int32 num_layers = 1
Number of grid layers to create anchors for.
optional float min_scale = 2
Scale of anchors corresponding to finest resolution.
optional float max_scale = 3
Scale of anchors corresponding to coarsest resolution
repeated float scales = 12
Can be used to override min_scale->max_scale, with an explicitly defined set of scales. If empty, then min_scale->max_scale is used.
repeated float aspect_ratios = 4
Aspect ratios for anchors at each grid point.
optional float interpolated_scale_aspect_ratio = 13
When this aspect ratio is greater than 0, then an additional anchor, with an interpolated scale is added with this aspect ratio.
optional bool reduce_boxes_in_lowest_layer = 5
Whether to use the following aspect ratio and scale combination for the layer with the finest resolution : (scale=0.1, aspect_ratio=1.0), (scale=min_scale, aspect_ration=2.0), (scale=min_scale, aspect_ratio=0.5).
optional float base_anchor_height = 6
The base anchor size in height dimension.
optional float base_anchor_width = 7
The base anchor size in width dimension.
repeated int32 height_stride = 8
Anchor stride in height dimension in pixels for each layer. The length of this field is expected to be equal to the value of num_layers.
repeated int32 width_stride = 9
Anchor stride in width dimension in pixels for each layer. The length of this field is expected to be equal to the value of num_layers.
repeated int32 height_offset = 10
Anchor height offset in pixels for each layer. The length of this field is expected to be equal to the value of num_layers.
repeated int32 width_offset = 11
Anchor width offset in pixels for each layer. The length of this field is expected to be equal to the value of num_layers.
message
ssd.proto:53Used in:
optional string type = 1
Type of ssd feature extractor.
optional float depth_multiplier = 2
The factor to alter the depth of the channels in the feature extractor.
optional int32 min_depth = 3
Minimum number of the channels in the feature extractor.
optional conv_hyperparams = 4
Hyperparameters for the feature extractor.
optional int32 pad_to_multiple = 5
The nearest multiple to zero-pad the input height and width dimensions to. For example, if pad_to_multiple = 2, input dimensions are zero-padded until the resulting dimensions are even.
optional bool batch_norm_trainable = 6
Whether to update batch norm parameters during training or not. When training with a relative small batch size (e.g. 1), it is desirable to disable batch norm update and use pretrained batch norm params. Note: Some feature extractors are used with canned arg_scopes (e.g resnet arg scopes). In these cases training behavior of batch norm variables may depend on both values of `batch_norm_trainable` and `is_training`. When canned arg_scopes are used with feature extractors `conv_hyperparams` will apply only to the additional layers that are added and are outside the canned arg_scope.
message
string_int_label_map.proto:22repeated item = 1
message
string_int_label_map.proto:9Used in:
optional string name = 1
String name. The most common practice is to set this to a MID or synsets id.
optional int32 id = 2
Integer id that maps to the string name above. Label ids should start from 1.
optional string display_name = 3
Human readable string label.
message
preprocessor.proto:257Normalizes an image by subtracting a mean from each channel.
Used in:
repeated float means = 1
The mean to subtract from each channel. Should be of same dimension of channels in the input image.
message
input_reader.proto:51An input reader that reads TF Example protos from local TFRecord files.
Used in:
repeated string input_path = 1
Path(s) to `TFRecordFile`s.
message
train.proto:9Message for configuring DetectionModel training jobs (train.py).
Used in:
optional uint32 batch_size = 1
Input queue batch size.
repeated data_augmentation_options = 2
Data augmentation options.
optional bool sync_replicas = 3
Whether to synchronize replicas during training.
optional uint32 keep_checkpoint_every_n_hours = 4
How frequently to keep checkpoints.
optional optimizer = 5
Optimizer used to train the DetectionModel.
optional float gradient_clipping_by_norm = 6
If greater than 0, clips gradients by this value.
optional string fine_tune_checkpoint = 7
Checkpoint to restore variables from. Typically used to load feature extractor variables trained outside of object detection.
optional bool from_detection_checkpoint = 8
Specifies if the finetune checkpoint is from an object detection model. If from an object detection model, the model being trained should have the same parameters with the exception of the num_classes parameter. If false, it assumes the checkpoint was a object classification model.
optional uint32 num_steps = 9
Number of steps to train the DetectionModel for. If 0, will train the model indefinitely.
optional float startup_delay_steps = 10
Number of training steps between replica startup. This flag must be set to 0 if sync_replicas is set to true.
optional float bias_grad_multiplier = 11
If greater than 0, multiplies the gradient of bias variables by this amount.
repeated string freeze_variables = 12
Variables that should not be updated during training.
optional int32 replicas_to_aggregate = 13
Number of replicas to aggregate before making parameter updates.
optional int32 batch_queue_capacity = 14
Maximum number of elements to store within a queue.
optional int32 num_batch_queue_threads = 15
Number of threads to use for batching.
optional int32 prefetch_queue_capacity = 16
Maximum capacity of the queue used to prefetch assembled batches.
optional bool merge_multiple_label_boxes = 17
If true, boxes with the same coordinates will be merged together. This is useful when each box can have multiple labels. Note that only Sigmoid classification losses should be used.
message
pipeline.proto:12Convenience message for configuring a training and eval pipeline. Allows all of the pipeline parameters to be configured from one file.
optional model = 1
optional train_config = 2
optional train_input_reader = 3
optional eval_config = 4
optional eval_input_reader = 5
message
hyperparams.proto:73Configuration proto for truncated normal initializer. See https://www.tensorflow.org/api_docs/python/tf/truncated_normal_initializer
Used in:
optional float mean = 1
optional float stddev = 2
message
hyperparams.proto:81Configuration proto for variance scaling initializer. See https://www.tensorflow.org/api_docs/python/tf/contrib/layers/ variance_scaling_initializer
Used in:
optional float factor = 1
optional bool uniform = 2
optional VarianceScalingInitializer.Mode mode = 3
Used in:
FAN_IN = 0
FAN_OUT = 1
FAN_AVG = 2
message
losses.proto:47Intersection over union location loss: 1 - IOU
Used in:
(message has no fields)
message
losses.proto:35L2 location loss: 0.5 * ||weight * (a - b)|| ^ 2
Used in:
optional bool anchorwise_output = 1
Output loss per anchor.
message
losses.proto:61Classification loss using a sigmoid function over class predictions.
Used in:
optional bool anchorwise_output = 1
Output loss per anchor.
message
losses.proto:41SmoothL1 (Huber) location loss: .5 * x ^ 2 if |x| < 1 else |x| - .5
Used in:
optional bool anchorwise_output = 1
Output loss per anchor.
message
losses.proto:77Classification loss using a softmax function over class predictions.
Used in:
optional bool anchorwise_output = 1
Output loss per anchor.
optional float logit_scale = 2
Scale logit (input) value before calculating softmax classification loss. Typically used for softmax distillation.