#tensorflow = 2.6.0
#tflite_model_maker = 0.3.2
#Python = 3.7.11 (on colab)
# load model spec
spec = model_spec.get('efficientdet_lite0')
# adjust model_spec's config for Tensorboard
setattr(spec.config, "sample_image", "/content/data2/IMG_0187.JPG")
setattr(spec.config, "profile", True)
setattr(spec.config, "img_summary_steps", 1) # default value is None
# Do train!
model = object_detector.create(train_data,
model_spec=spec,
batch_size=8,
train_whole_model=False,
epochs = 100,
validation_data = valid,
do_train = True)
model.evaluate(test)
I think "img_summary_steps" option is for detected results visualization per 1epoch on Tensorboard but when i use "img_summary_steps" occurred error :<
training well when i don't use "img_summary_steps" option but not show up Detection result on Tensorboard
this is result when i print(spec.config)
act_type: relu6
alpha: 0.25
anchor_scale: 3.0
apply_bn_for_resampling: true
aspect_ratios:
- 1.0
- 2.0
- 0.5
autoaugment_policy: null
backbone_config: null
backbone_name: efficientnet-lite0
batch_size: 64
box_class_repeats: 3
box_loss_weight: 50.0
ckpt_var_scope: null
clip_gradients_norm: 10.0
conv_after_downsample: false
conv_bn_act_pattern: false
data_format: channels_last
dataset_type: null
debug: false
delta: 0.1
drop_remainder: true
first_lr_drop_epoch: 200.0
fpn_cell_repeats: 3
fpn_config: null
fpn_name: null
fpn_num_filters: 64
fpn_weight_method: sum
gamma: 1.5
grad_checkpoint: false
grid_mask: false
heads:
- object_detection
image_size: !!python/tuple
- 320
- 320
img_summary_steps: null
input_rand_hflip: true
iou_loss_type: null
iou_loss_weight: 1.0
is_training_bn: true
jitter_max: 2.0
jitter_min: 0.1
label_map: null
label_smoothing: 0.0
learning_rate: 0.08
loss_scale: null
lr_decay_method: cosine
lr_warmup_epoch: 1.0
lr_warmup_init: 0.008
map_freq: 5
max_instances_per_image: 100
max_level: 7
mean_rgb: 127.0
min_level: 3
mixed_precision: false
model_dir: /tmp/tmpaziqa0l2
model_name: efficientdet-lite0
model_optimizations: {}
momentum: 0.9
moving_average_decay: 0
name: efficientdet-lite0
nms_configs:
iou_thresh: null
max_nms_inputs: 0
max_output_size: 100
method: gaussian
pyfunc: false
score_thresh: 0.0
sigma: null
num_classes: 90
num_epochs: 50
num_scales: 3
optimizer: sgd
poly_lr_power: 0.9
positives_momentum: null
profile: false
regenerate_source_id: false
sample_image: null
save_freq: epoch
scale_range: false
second_lr_drop_epoch: 250.0
seg_num_classes: 3
separable_conv: true
skip_crowd_during_training: true
skip_mismatch: true
stddev_rgb: 128.0
steps_per_execution: 1
strategy: null
survival_prob: null
target_size: null
tf_random_seed: 111111
tflite_max_detections: 25
use_keras_model: true
var_freeze_expr: (efficientnet|fpn_cells|resample_p6)
verbose: 0
weight_decay: 4.0e-05
