Disable augmentation in tensorflow training pipeline - tensorflow

Question

I googled around a bit but I only found questions about enabling data augmentation.
I followed this tutorial but with my own dataset (only one class). I already performed data augmentation on my dataset so I deleted the responsible lines from the pipeline.config.
Now my pipeline looks like this
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 640
width: 640
}
}
feature_extractor {
type: "ssd_resnet50_v1_fpn_keras"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 0.00039999998989515007
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.029999999329447746
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019165
scale: true
epsilon: 0.0010000000474974513
}
}
override_base_feature_extractor_hyperparams: true
fpn {
min_level: 3
max_level: 7
}
}
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
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 0.00039999998989515007
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019165
scale: true
epsilon: 0.0010000000474974513
}
}
depth: 256
num_layers_before_predictor: 4
kernel_size: 3
class_prediction_bias_init: -4.599999904632568
}
}
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: 2
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 9.99999993922529e-09
iou_threshold: 0.6000000238418579
max_detections_per_class: 100
max_total_detections: 100
use_static_shapes: false
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
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
}
}
train_config {
batch_size: 1
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.03999999910593033
total_steps: 25000
warmup_learning_rate: 0.013333000242710114
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: "/home/sally/work/training/TensorFlow/workspace/pre-trained-models/ssd_resnet50_v1_fpn_640x640_coco17_tpu-8/checkpoint/ckpt-0"
num_steps: 25000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "detection"
use_bfloat16: false
fine_tune_checkpoint_version: V2
}
train_input_reader {
label_map_path: "/home/sally/work/training/TensorFlow/workspace/annotations/label_map.pbtxt"
tf_record_input_reader {
input_path: "/home/sally/work/training/TensorFlow/workspace/annotations/train.record"
}
}
eval_config {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: "/home/sally/work/training/TensorFlow/workspace/annotations/label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "/home/sally/work/training/TensorFlow/workspace/annotations/test.record"
}
}
I started the training but with tensorboard I can see that the training images are very very distorted.
For reference normal images look like this
As you can see I try to detect Kellogs boxes. The dataset is generated using blender (soda can and fence are to have some sort of decoy objects and to be able to cover the boxes partially)
Now my question: How do I disably any sort of data augmentation in the object detection api?
The map is very very low because of these distorted images used during the training process.

Answer 1

This is an issue with the normalization of the image. It does not affect your training.
However, if you want the images to be displayed correctly in tensorboard, then normalize them between (0, 1). Check this link for some possible changes.
Note: normalizing between (-1, 1) has been reported to create the same issue.