training_config { checkpoint: "/workspace/tao-experiments/efficientdet/pretrained_efficientdet_vefficientnet_b2/efficientnet_b2.hdf5" train_batch_size: 8 iterations_per_loop: 10 checkpoint_period: 10 num_examples_per_epoch: 75000 num_epochs: 30 tf_random_seed: 42 lr_warmup_epoch: 5 lr_warmup_init: 0.00005 learning_rate: 0.005 amp: True moving_average_decay: 0.9999 l2_weight_decay: 0.00004 l1_weight_decay: 0.0 } dataset_config { num_classes: 2 image_size: "544,960" training_file_pattern: "/workspace/tao-experiments/data/tfrecords_efficientnet/train-*" validation_file_pattern: "/workspace/tao-experiments/data/tfrecords_efficientnet/val-*" validation_json_file: "/workspace/tao-experiments/data/training_v6_coco_crowd/testing.json" #validation_json_file: "/workspace/tao-experiments/data/training_v6_coco_crowd/testing1.json" max_instances_per_image: 400 skip_crowd_during_training: True } model_config { model_name: 'efficientdet-d2' min_level: 3 max_level: 7 num_scales: 3 } augmentation_config { rand_hflip: True random_crop_min_scale: 0.1 random_crop_min_scale: 2.0 } eval_config { eval_batch_size: 8 eval_epoch_cycle: 10 eval_samples: 4196 min_score_thresh: 0.0 max_detections_per_image: 400 }