问题
I have defined a pascal_label_map.pbtext with 824 classes to create TFRecord files from my JPEG dataset with Pascal VOC style annotations with create_pascal_tf_record.py.
The script seems to generate these TFRecords correctly (e.g. I checked that all classes from pascal_label_map.pbtext occur in the annotations and that each JPEG comes with the correct annotation). But when I start object_detection/model_main.py I see the following:
WARNING:root:The following classes have no ground truth examples:
[
2 3 5 7 9 10 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 30 35 36 37 38 40 42 43 44 47 48 49 51 52 53
55 58 59 60 61 62 64 65 69 70 71 73 74 75 77 78 79 81
82 84 85 86 87 88 90 91 92 93 94 95 96 97 98 99 100 101
102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137
138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155
156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173
174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191
192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209
210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227
228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245
246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263
264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281
282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299
300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317
318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335
336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353
354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371
372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389
390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407
408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425
426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443
444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461
462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479
480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497
498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515
516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533
534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551
552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569
570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587
588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605
606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623
624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641
642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659
660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677
678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695
696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713
714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731
732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749
750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767
768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785
786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803
804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821
822 823 824]
How can I fix this?
I have tried both python2.7 and python3.7 (installed with anaconda in bash on Ubuntu in Windows). Instead of model_main.py I tried object_detection/legacy/train.py and object_detection/legacy/eval.py.
train.py seemed to run correctly.
When I opened tensorboard after running train.py and eval.py, I noticed that neither the images had any object detection boxes nor any correct ground-truth. Expect for one or two cases, the incorrect ground truth label was the one corresponding to 1 in pascal_label_map.pbtext.
The bounding box coordinates are correct though.
This is my model .config file (with the right pathes of course):
model {
faster_rcnn {
num_classes: 821
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 600
max_dimension: 1024
}
}
feature_extractor {
type: 'faster_rcnn_inception_v2'
first_stage_features_stride: 16
}
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: 300
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
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 300
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 1
optimizer {
momentum_optimizer: {
learning_rate: {
manual_step_learning_rate {
initial_learning_rate: 0.0002
schedule {
step: 900000
learning_rate: .00002
}
schedule {
step: 1200000
learning_rate: .000002
}
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
#fine_tune_checkpoint: "PATH_TO/models/model/model.ckpt"
#from_detection_checkpoint: true
#load_all_detection_checkpoint_vars: true
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the COCO dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
num_steps: 5000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_vertical_flip {
}
}
data_augmentation_options {
random_rotation90 {
}
}
}
train_input_reader {
label_map_path: "PATH_TO/data/pascal_label_map.pbtxt"
tf_record_input_reader {
input_path:"PATH_TO/data/pascal_train.record-?????-of-00010"
}
}
eval_config {
num_examples: 1886
# Note: The below line limits the evaluation process to 100 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 1886
#use_moving_averages: false
metrics_set: "pascal_voc_detection_metrics"
}
eval_input_reader {
label_map_path: "PATH_TO/data/pascal_label_map.pbtxt"
shuffle: false
num_readers: 10
tf_record_input_reader {
input_path: "PATH_TO/data/pascal_val.record-?????-of-00010"
}
}
The "pascal_voc_detection_metrics" don't seems to work either.
来源：https://stackoverflow.com/questions/55904690/how-to-fix-the-following-classes-have-no-ground-truth-examples-when-running-ob