EfficientDet D1 : "object_detection.protos.FixedShapeResizer" has no field named "min_dimension"

I am trying to use the "fix_shape_resizer" for EfficientDet D1 640*640.

Here is my config file -
model {
ssd {
num_classes: 14
image_resizer {
fixed_shape_resizer {
min_dimension: 512
max_dimension: 512
pad_to_max_dimension: false
}
}
feature_extractor {
type: "ssd_efficientnet-b3_bifpn_keras"
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.029999999329447746
}
}
activation: SWISH
batch_norm {
decay: 0.9900000095367432
scale: true
epsilon: 0.0010000000474974513
}
force_use_bias: true
}
bifpn {
min_level: 3
max_level: 7
num_iterations: 6
num_filters: 160
}
}
box_coder {
faster_rcnn_box_coder {
y_scale: 1.0
x_scale: 1.0
height_scale: 1.0
width_scale: 1.0
}
}
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: 3.9999998989515007e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: SWISH
batch_norm {
decay: 0.9900000095367432
scale: true
epsilon: 0.0010000000474974513
}
force_use_bias: true
}
depth: 160
num_layers_before_predictor: 4
kernel_size: 3
class_prediction_bias_init: -4.599999904632568
use_depthwise: true
}
}
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: 3
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 9.99999993922529e-09
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 1.5
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
add_background_class: false
}
}
train_config {
batch_size: 128
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 512
scale_min: 0.10000000149011612
scale_max: 2.0
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.07999999821186066
total_steps: 300000
warmup_learning_rate: 0.0010000000474974513
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: "checkpoint/ckpt-0"
num_steps: 300000
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
fine_tune_checkpoint_version: V2
}
train_input_reader: {
label_map_path: "/opt/ml/input/data/train/label_map.pbtxt"
tf_record_input_reader {
input_path: "/opt/ml/input/data/train/train.records"
}
}

eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}

eval_input_reader: {
label_map_path: "/opt/ml/input/data/train/label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "/opt/ml/input/data/train/validation.records"
}
}

The error I am getting is as follows -
google.protobuf.text_format.ParseError: 6:9 : Message type "object_detection.protos.FixedShapeResizer" has no field named "min_dimension".