Tensorflow Object Detection API

Forked from https://github.com/tensorflow/models/tree/master/object_detection

Updates

Add from_detection_checkpoint parameter in train_config

Old:

bool from_detection_checkpoint

• True: the checkpoint was an object detection model that have the same parameters with the exception of the num_classes parameter.

• False: the checkpoint was a object classification model.

New:

uint32 from_detection_checkpoint

• 3: don't load any variables.

• 2: load all variables.

• 1: load feature extractor variables from an object detection model, same as True.

• 0: load feature extractor variables from a object classification model, same as False.

Modified files:

• trainer.py

• core/model.py

• protos/train.proto

• meta_architectures/ssd_meta_arch.py

• meta_architectures/faster_rcnn_meta_arch.py

Remove some summaries when training

Remove summaries about histograms and first_clone_scope when training.

Modified files:

• trainer.py

Add gpu_allow_growth parameter in eval.py

Add gpu_allow_growth parameter in eval.py, default value is True which means attempting to allocate only as much GPU memory based on runtime allocations.

Modified files:

• eval.py

• evaluator.py

• eval_util.py

Add gpu_allow_growth parameter in train.py

Add gpu_allow_growth parameter in train.py, default value is True which means attempting to allocate only as much GPU memory based on runtime allocations.

Modified files:

• train.py

• trainer.py

Add max_to_keep parameter in train_config

Add max_to_keep parameter in train_config, default value is 5 which means the 5 most recent checkpoint files are kept. If 0, all checkpoint files are kept.

Modified files:

• trainer.py

• protos/train.proto

Add FocalSigmoidClassificationLoss in model

In config, model -> loss -> classification_loss can be focal_sigmoid, parameters: anchorwise_output, gamma.

Reference: https://arxiv.org/pdf/1708.02002.pdf

Modified files:

• core/losses.py

• builders/losses_builder.py

• protos/losses.proto