I am not able to detect 1000's of instances of object in single image by using Mask-RCNN model.

I have trained Mask-RCNN model with dataset in which in every single image, I am having 800-1000 instances of object which are small but detectable. And I am to train the model successfully.

But when I am using this model to test on same set of images, I am only able to detect the instance of object from any one side till 3/4th or 1/2 of image. But not all the instances of object.

What I feel that my model is able to detect the object but it is failing to detect all the instance. Kindly help on this.

Following is my config file and I have changed the path for labelmap.pbtxt and dataset path as per requirement.
I have added few parameters such as max_number_of_boxes, max_num_boxes_to_visualize and tuned it to upper limits value.
I have also tuned max_detections_per_class, max_total_detections and first_stage_max_proposals parameters to its maximum value after which incrementing these values throws memory error.

model {
faster_rcnn {
number_of_stages: 3
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 1024
width: 1024
}
}
feature_extractor {
type: 'faster_rcnn_inception_resnet_v2_keras'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 1000
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 17
maxpool_kernel_size: 1
maxpool_stride: 1
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
mask_height: 33
mask_width: 33
mask_prediction_conv_depth: 0
mask_prediction_num_conv_layers: 2
conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
predict_instance_masks: true
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 350
max_total_detections: 350
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
second_stage_mask_prediction_loss_weight: 4.0
resize_masks: false
}
}

train_config: {
batch_size: 1
max_number_of_boxes: 200
num_steps: 5000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.008
total_steps: 5000
warmup_learning_rate: 0.0
warmup_steps: 500
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED"
fine_tune_checkpoint_type: "detection"
use_bfloat16: false
data_augmentation_options {
random_horizontal_flip {
}
}
}

train_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED"
max_number_of_boxes: 200
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED"
}
load_instance_masks: true
mask_type: PNG_MASKS
}

eval_config: {
metrics_set: "coco_mask_metrics"
eval_instance_masks: true
use_moving_averages: false
batch_size: 1
include_metrics_per_category: false
max_num_boxes_to_visualize: 200
}

eval_input_reader: {
label_map_path: "PATH_TO_BE_CONFIGURED"
max_number_of_boxes: 200
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED"
}
load_instance_masks: true
mask_type: PNG_MASKS
}