I have been using Tensorflow Object Detection API on my own dataset. While training, I want to know how well the NN is learning from the Training set. So, I want to run an evaluation on both training and eval set and get accuracy (mAP) respectively during the training sessions.
My config file:
model {
faster_rcnn {
num_classes: 50
image_resizer {
fixed_shape_resizer {
height: 960
width: 960
}
}
number_of_stages: 3
feature_extractor {
type: 'faster_rcnn_resnet101'
first_stage_features_stride: 8
}
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: 8
width_stride: 8
}
}
first_stage_atrous_rate: 2
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.00999999977648
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.699999988079
first_stage_max_proposals: 100
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
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
}
}
}
conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.00999999977648
}
}
}
predict_instance_masks: true
mask_height: 33
mask_width: 33
mask_prediction_conv_depth: 0
mask_prediction_num_conv_layers: 4
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.300000011921
iou_threshold: 0.600000023842
max_detections_per_class: 100
max_total_detections: 100
}
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
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.003
schedule {
step: 3000
learning_rate: 0.00075
}
schedule {
step: 6000
learning_rate: 0.000300000014249
}
schedule {
step: 15000
learning_rate: 0.000075
}
schedule {
step: 18000
learning_rate: 0.0000314249
}
schedule {
step: 900000
learning_rate: 2.99999992421e-05
}
schedule {
step: 1200000
learning_rate: 3.00000010611e-06
}
}
}
momentum_optimizer_value: 0.899999976158
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint: "./mask_rcnn_resnet101_atrous_coco/model.ckpt"
from_detection_checkpoint: true
num_steps: 200000
data_augmentation_options {
random_horizontal_flip {
}
}
}
train_input_reader: {
label_map_path: "./map901_label_map.pbtxt"
load_instance_masks: true
mask_type: PNG_MASKS
tf_record_input_reader {
input_path: ["./my_coco_train.record-?????-of-00005"]
}
}
eval_config: {
num_examples: 8000
max_evals: 100
num_visualizations: 25
}
eval_input_reader: {
label_map_path: "./map901_label_map.pbtxt"
shuffle: false
load_instance_masks: true
mask_type: PNG_MASKS
num_readers: 1
tf_record_input_reader {
input_path: ["./my_coco_val.record-?????-of-00001"]
}
}
I ran the script with these parameters
python model_main.py --alsologtostderr \
--pipeline_config_path=${PIPELINE_CONFIG_PATH} \
--model_dir=${TRAIN_DIR} \
--num_train_steps=24000 \
--sample_1_of_n_eval_on_train_examples=25 \
--num_eval_steps=100 \
--sample_1_of_n_eval_examples=1
I think this will run an evaluation of Eval examples. To evaluate training data (to check how many features captured from training) I have added --eval_training_data=True to the parameters.
I can not add the "eval_training_data" on the go. I need to run 2 different training sessions.
Interestingly, with "eval_training_data" parameter added I got,
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.165
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.281
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.167
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.051
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.109
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.164
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.202
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.057
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.141
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.236
Whereas without "eval_training_data" I got
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.168
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.283
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.173
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.049
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.108
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.170
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.056
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.139
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.248
I just got confused. My questions are:
- Does not "eval_training_data" force object detection API to run evaluation on the Training set?
- Why in my case both scores are almost the same, in some cases eval score is better?
- Which parameters need to add so that it evaluates both training and eval set separately during a training session and print it?
from TensorFlow object detection API evaluate training performance
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