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data-matrix-detection-benchmark

Benchmark for the detection of data matrix landmarks using Deep Learning architectures.

Table of Contents

Overview

This repo is a comparison between Faster RCNN, SSD512 and YOLOV3(608) for Data Matrix detection. The performance comparison between all models is also performed. The training set images size from the original dataset were 8000*6000. The original training set images were reduced 4 times due to the faster forward propagation of the single shot methods and the way how Python deals with memory deallocation (see Python Garbage Collection).

Hardware

Training and Evaluation: Nvidia RTX2080ti

Faster RCNN

Weights:

  • Resnet50 FPN: COCO
  • Resnet50 and MobileNetV2: Imagenet

Resnet50FPN pretrained

To train:

python scripts/train_faster.py --batch_size 4 --learning_rate 2.5e-4 --epochs 10 --feature_extractor resnet50fpn

To evaluate:

python scripts/eval_faster.py --feature_extractor resnet50fpn --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.681
Average Precision (AP) 0.50 all 100 0.924
Average Precision (AP) 0.75 all 100 0.840
Average Precision (AP) 0.50:0.95 small 100 0.480
Average Precision (AP) 0.50:0.95 medium 100 0.676
Average Precision (AP) 0.50:0.95 large 100 0.737
Average Recall (AR) 0.50:0.95 all 1 0.311
Average Recall (AR) 0.50:0.95 all 10 0.752
Average Recall (AR) 0.50:0.95 all 100 0.725
Average Recall (AR) 0.50:0.95 small 100 0.642
Average Recall (AR) 0.50:0.95 medium 100 0.735
Average Recall (AR) 0.50:0.95 large 100 0.798

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.728
Average Precision (AP) 0.50 all 100 0.953
Average Precision (AP) 0.75 all 100 0.905
Average Precision (AP) 0.50:0.95 small 100 0.210
Average Precision (AP) 0.50:0.95 medium 100 0.710
Average Precision (AP) 0.50:0.95 large 100 0.809
Average Recall (AR) 0.50:0.95 all 1 0.140
Average Recall (AR) 0.50:0.95 all 10 0.740
Average Recall (AR) 0.50:0.95 all 100 0.772
Average Recall (AR) 0.50:0.95 small 100 0.452
Average Recall (AR) 0.50:0.95 medium 100 0.754
Average Recall (AR) 0.50:0.95 large 100 0.844

Resnet50FPN from scratch

To train:

python scripts/train_faster.py --batch_size 4 --learning_rate 2.5e-4 --epochs 30 --feature_extractor resnet50fpn --pretrained False

To evaluate:

python scripts/eval_faster.py --feature_extractor resnet50fpn --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.599
Average Precision (AP) 0.50 all 100 0.780
Average Precision (AP) 0.75 all 100 0.716
Average Precision (AP) 0.50:0.95 small 100 0.337
Average Precision (AP) 0.50:0.95 medium 100 0.529
Average Precision (AP) 0.50:0.95 large 100 0.736
Average Recall (AR) 0.50:0.95 all 1 0.280
Average Recall (AR) 0.50:0.95 all 10 0.648
Average Recall (AR) 0.50:0.95 all 100 0.648
Average Recall (AR) 0.50:0.95 small 100 0.386
Average Recall (AR) 0.50:0.95 medium 100 0.568
Average Recall (AR) 0.50:0.95 large 100 0.790

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.722
Average Precision (AP) 0.50 all 100 0.906
Average Precision (AP) 0.75 all 100 0.854
Average Precision (AP) 0.50:0.95 small 100 0.222
Average Precision (AP) 0.50:0.95 medium 100 0.698
Average Precision (AP) 0.50:0.95 large 100 0.815
Average Recall (AR) 0.50:0.95 all 1 0.145
Average Recall (AR) 0.50:0.95 all 10 0.735
Average Recall (AR) 0.50:0.95 all 100 0.753
Average Recall (AR) 0.50:0.95 small 100 0.396
Average Recall (AR) 0.50:0.95 medium 100 0.730
Average Recall (AR) 0.50:0.95 large 100 0.851

framerate: 5.4fps

Resnet50 pretrained

To train:

python scripts/train_faster.py --batch_size 2 --learning_rate 2.5e-4 --epochs 30 --feature_extractor resnet50

To evaluate:

python scripts/eval_faster.py --feature_extractor resnet50 --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.543
Average Precision (AP) 0.50 all 100 0.795
Average Precision (AP) 0.75 all 100 0.595
Average Precision (AP) 0.50:0.95 small 100 0.186
Average Precision (AP) 0.50:0.95 medium 100 0.455
Average Precision (AP) 0.50:0.95 large 100 0.707
Average Recall (AR) 0.50:0.95 all 1 0.277
Average Recall (AR) 0.50:0.95 all 10 0.610
Average Recall (AR) 0.50:0.95 all 100 0.611
Average Recall (AR) 0.50:0.95 small 100 0.316
Average Recall (AR) 0.50:0.95 medium 100 0.524
Average Recall (AR) 0.50:0.95 large 100 0.768

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.652
Average Precision (AP) 0.50 all 100 0.894
Average Precision (AP) 0.75 all 100 0.750
Average Precision (AP) 0.50:0.95 small 100 0.032
Average Precision (AP) 0.50:0.95 medium 100 0.603
Average Precision (AP) 0.50:0.95 large 100 0.806
Average Recall (AR) 0.50:0.95 all 1 0.143
Average Recall (AR) 0.50:0.95 all 10 0.665
Average Recall (AR) 0.50:0.95 all 100 0.688
Average Recall (AR) 0.50:0.95 small 100 0.145
Average Recall (AR) 0.50:0.95 medium 100 0.644
Average Recall (AR) 0.50:0.95 large 100 0.840

Resnet50 from scratch

To train:

python scripts/train_faster.py --batch_size 2 --learning_rate 1e-3 --epochs 200 --feature_extractor resnet50 --pretrained False

To evaluate:

python scripts/eval_faster.py --feature_extractor resnet50 --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.499
Average Precision (AP) 0.50 all 100 0.724
Average Precision (AP) 0.75 all 100 0.547
Average Precision (AP) 0.50:0.95 small 100 0.166
Average Precision (AP) 0.50:0.95 medium 100 0.412
Average Precision (AP) 0.50:0.95 large 100 0.661
Average Recall (AR) 0.50:0.95 all 1 0.255
Average Recall (AR) 0.50:0.95 all 10 0.555
Average Recall (AR) 0.50:0.95 all 100 0.555
Average Recall (AR) 0.50:0.95 small 100 0.255
Average Recall (AR) 0.50:0.95 medium 100 0.463
Average Recall (AR) 0.50:0.95 large 100 0.718

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.618
Average Precision (AP) 0.50 all 100 0.855
Average Precision (AP) 0.75 all 100 0.730
Average Precision (AP) 0.50:0.95 small 100 0.033
Average Precision (AP) 0.50:0.95 medium 100 0.548
Average Precision (AP) 0.50:0.95 large 100 0.794
Average Recall (AR) 0.50:0.95 all 1 0.141
Average Recall (AR) 0.50:0.95 all 10 0.640
Average Recall (AR) 0.50:0.95 all 100 0.661
Average Recall (AR) 0.50:0.95 small 100 0.112
Average Recall (AR) 0.50:0.95 medium 100 0.614
Average Recall (AR) 0.50:0.95 large 100 0.824

framerate: 3.9

MobileNetV2 pretrained

To train:

python scripts/train_faster.py --batch_size 4 --learning_rate 2.5e-4 --epochs 30 --feature_extractor mobilenetv2

To evaluate:

python scripts/eval_faster.py --feature_extractor mobilenetv2 --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.458
Average Precision (AP) 0.50 all 100 0.745
Average Precision (AP) 0.75 all 100 0.468
Average Precision (AP) 0.50:0.95 small 100 0.023
Average Precision (AP) 0.50:0.95 medium 100 0.381
Average Precision (AP) 0.50:0.95 large 100 0.624
Average Recall (AR) 0.50:0.95 all 1 0.245
Average Recall (AR) 0.50:0.95 all 10 0.519
Average Recall (AR) 0.50:0.95 all 100 0.519
Average Recall (AR) 0.50:0.95 small 100 0.152
Average Recall (AR) 0.50:0.95 medium 100 0.461
Average Recall (AR) 0.50:0.95 large 100 0.668

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.549
Average Precision (AP) 0.50 all 100 0.857
Average Precision (AP) 0.75 all 100 0.595
Average Precision (AP) 0.50:0.95 small 100 0.004
Average Precision (AP) 0.50:0.95 medium 100 0.462
Average Precision (AP) 0.50:0.95 large 100 0.767
Average Recall (AR) 0.50:0.95 all 1 0.137
Average Recall (AR) 0.50:0.95 all 10 0.564
Average Recall (AR) 0.50:0.95 all 100 0.586
Average Recall (AR) 0.50:0.95 small 100 0.048
Average Recall (AR) 0.50:0.95 medium 100 0.511
Average Recall (AR) 0.50:0.95 large 100 0.804

MobileNetV2 from scratch

To train:

python scripts/train_faster.py --batch_size 4 --learning_rate 1e-3 --epochs 200 --feature_extractor mobilenetv2 --pretrained False

To evaluate:

python scripts/eval_faster.py --feature_extractor mobilenetv2 --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.394
Average Precision (AP) 0.50 all 100 0.619
Average Precision (AP) 0.75 all 100 0.420
Average Precision (AP) 0.50:0.95 small 100 0.055
Average Precision (AP) 0.50:0.95 medium 100 0.285
Average Precision (AP) 0.50:0.95 large 100 0.578
Average Recall (AR) 0.50:0.95 all 1 0.215
Average Recall (AR) 0.50:0.95 all 10 0.449
Average Recall (AR) 0.50:0.95 all 100 0.449
Average Recall (AR) 0.50:0.95 small 100 0.077
Average Recall (AR) 0.50:0.95 medium 100 0.350
Average Recall (AR) 0.50:0.95 large 100 0.636

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.522
Average Precision (AP) 0.50 all 100 0.783
Average Precision (AP) 0.75 all 100 0.583
Average Precision (AP) 0.50:0.95 small 100 0.000
Average Precision (AP) 0.50:0.95 medium 100 0.432
Average Precision (AP) 0.50:0.95 large 100 0.743
Average Recall (AR) 0.50:0.95 all 1 0.137
Average Recall (AR) 0.50:0.95 all 10 0.552
Average Recall (AR) 0.50:0.95 all 100 0.566
Average Recall (AR) 0.50:0.95 small 100 0.009
Average Recall (AR) 0.50:0.95 medium 100 0.494
Average Recall (AR) 0.50:0.95 large 100 0.780

frame rate: 7.7 fps

SSD512

Resnet50

To train:

python scripts/train_ssd.py --model ssd512 --batch_size 16 --learning_rate 1e-3 --weight_decay 4e-5 --epochs 300 --feature_extractor resnet50

To evaluate:

python scripts/eval_ssd.py --model ssd512 --feature_extractor resnet50 --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.386
Average Precision (AP) 0.50 all 100 0.587
Average Precision (AP) 0.75 all 100 0.446
Average Precision (AP) 0.50:0.95 small 100 0.000
Average Precision (AP) 0.50:0.95 medium 100 0.241
Average Precision (AP) 0.50:0.95 large 100 0.539
Average Recall (AR) 0.50:0.95 all 1 0.207
Average Recall (AR) 0.50:0.95 all 10 0.440
Average Recall (AR) 0.50:0.95 all 100 0.440
Average Recall (AR) 0.50:0.95 small 100 0.000
Average Recall (AR) 0.50:0.95 medium 100 0.262
Average Recall (AR) 0.50:0.95 large 100 0.614

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.455
Average Precision (AP) 0.50 all 100 0.683
Average Precision (AP) 0.75 all 100 0.546
Average Precision (AP) 0.50:0.95 small 100 0.000
Average Precision (AP) 0.50:0.95 medium 100 0.377
Average Precision (AP) 0.50:0.95 large 100 0.649
Average Recall (AR) 0.50:0.95 all 1 0.128
Average Recall (AR) 0.50:0.95 all 10 0.490
Average Recall (AR) 0.50:0.95 all 100 0.490
Average Recall (AR) 0.50:0.95 small 100 0.000
Average Recall (AR) 0.50:0.95 medium 100 0.419
Average Recall (AR) 0.50:0.95 large 100 0.691

framerate: 48.3 fps

MobileNetV2

To train:

python scripts/train_ssd.py --model ssd512 --batch_size 16 --learning_rate 1e-3 --weight_decay 4e-5 --epochs 300 --feature_extractor mobilenetv2

To evaluate:

python scripts/eval_ssd.py --model ssd512 --feature_extractor mobilenetv2 --state_dict <PATH to model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.246
Average Precision (AP) 0.50 all 100 0.422
Average Precision (AP) 0.75 all 100 0.257
Average Precision (AP) 0.50:0.95 small 100 0.000
Average Precision (AP) 0.50:0.95 medium 100 0.079
Average Precision (AP) 0.50:0.95 large 100 0.329
Average Recall (AR) 0.50:0.95 all 1 0.162
Average Recall (AR) 0.50:0.95 all 10 0.333
Average Recall (AR) 0.50:0.95 all 100 0.333
Average Recall (AR) 0.50:0.95 small 100 0.000
Average Recall (AR) 0.50:0.95 medium 100 0.080
Average Recall (AR) 0.50:0.95 large 100 0.449

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.255
Average Precision (AP) 0.50 all 100 0.441
Average Precision (AP) 0.75 all 100 0.270
Average Precision (AP) 0.50:0.95 small 100 0.000
Average Precision (AP) 0.50:0.95 medium 100 0.128
Average Precision (AP) 0.50:0.95 large 100 0.529
Average Recall (AR) 0.50:0.95 all 1 0.108
Average Recall (AR) 0.50:0.95 all 10 0.277
Average Recall (AR) 0.50:0.95 all 100 0.277
Average Recall (AR) 0.50:0.95 small 100 0.000
Average Recall (AR) 0.50:0.95 medium 100 0.151
Average Recall (AR) 0.50:0.95 large 100 0.569

framerate: 67fps

YOLO

Input size: width = 896

v3

To train:

python scripts/train_yolo.py --model yolov3 --batch_size 4 --epochs 400

To evaluate:

python scripts/eval_yolo.py --model yolov3 --batch_size 4 --state_dict <model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.350
Average Precision (AP) 0.50 all 100 0.642
Average Precision (AP) 0.75 all 100 0.325
Average Precision (AP) 0.50:0.95 small 100 0.265
Average Precision (AP) 0.50:0.95 medium 100 0.545
Average Precision (AP) 0.50:0.95 large 100 0.800
Average Recall (AR) 0.50:0.95 all 1 0.189
Average Recall (AR) 0.50:0.95 all 10 0.443
Average Recall (AR) 0.50:0.95 all 100 0.451
Average Recall (AR) 0.50:0.95 small 100 0.361
Average Recall (AR) 0.50:0.95 medium 100 0.643
Average Recall (AR) 0.50:0.95 large 100 0.800

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.543
Average Precision (AP) 0.50 all 100 0.829
Average Precision (AP) 0.75 all 100 0.625
Average Precision (AP) 0.50:0.95 small 100 0.385
Average Precision (AP) 0.50:0.95 medium 100 0.658
Average Precision (AP) 0.50:0.95 large 100 0.725
Average Recall (AR) 0.50:0.95 all 1 0.127
Average Recall (AR) 0.50:0.95 all 10 0.586
Average Recall (AR) 0.50:0.95 all 100 0.624
Average Recall (AR) 0.50:0.95 small 100 0.507
Average Recall (AR) 0.50:0.95 medium 100 0.715
Average Recall (AR) 0.50:0.95 large 100 0.768

v3 mosaic aug

To train:

python scripts/train_yolo.py --model yolov3 --batch_size 4 --epochs 400 --imgs_rect False

To evaluate:

python scripts/eval_yolo.py --model yolov3 --batch_size 4 --state_dict <model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.375
Average Precision (AP) 0.50 all 100 0.646
Average Precision (AP) 0.75 all 100 0.380
Average Precision (AP) 0.50:0.95 small 100 0.276
Average Precision (AP) 0.50:0.95 medium 100 0.588
Average Precision (AP) 0.50:0.95 large 100 0.900
Average Recall (AR) 0.50:0.95 all 1 0.209
Average Recall (AR) 0.50:0.95 all 10 0.453
Average Recall (AR) 0.50:0.95 all 100 0.463
Average Recall (AR) 0.50:0.95 small 100 0.367
Average Recall (AR) 0.50:0.95 medium 100 0.668
Average Recall (AR) 0.50:0.95 large 100 0.900

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.594
Average Precision (AP) 0.50 all 100 0.857
Average Precision (AP) 0.75 all 100 0.707
Average Precision (AP) 0.50:0.95 small 100 0.435
Average Precision (AP) 0.50:0.95 medium 100 0.704
Average Precision (AP) 0.50:0.95 large 100 0.770
Average Recall (AR) 0.50:0.95 all 1 0.132
Average Recall (AR) 0.50:0.95 all 10 0.626
Average Recall (AR) 0.50:0.95 all 100 0.663
Average Recall (AR) 0.50:0.95 small 100 0.544
Average Recall (AR) 0.50:0.95 medium 100 0.755
Average Recall (AR) 0.50:0.95 large 100 0.804

framerate: 39.8 fps

v3 SPP

To train:

python scripts/train_yolo.py --model yolov3_spp --batch_size 4 --epochs 400

To evaluate:

python scripts/eval_yolo.py --model yolov3_spp --batch_size 4 --state_dict <model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.364
Average Precision (AP) 0.50 all 100 0.665
Average Precision (AP) 0.75 all 100 0.366
Average Precision (AP) 0.50:0.95 small 100 0.267
Average Precision (AP) 0.50:0.95 medium 100 0.566
Average Precision (AP) 0.50:0.95 large 100 0.800
Average Recall (AR) 0.50:0.95 all 1 0.198
Average Recall (AR) 0.50:0.95 all 10 0.459
Average Recall (AR) 0.50:0.95 all 100 0.467
Average Recall (AR) 0.50:0.95 small 100 0.378
Average Recall (AR) 0.50:0.95 medium 100 0.658
Average Recall (AR) 0.50:0.95 large 100 0.800

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.568
Average Precision (AP) 0.50 all 100 0.851
Average Precision (AP) 0.75 all 100 0.659
Average Precision (AP) 0.50:0.95 small 100 0.406
Average Precision (AP) 0.50:0.95 medium 100 0.687
Average Precision (AP) 0.50:0.95 large 100 0.755
Average Recall (AR) 0.50:0.95 all 1 0.132
Average Recall (AR) 0.50:0.95 all 10 0.607
Average Recall (AR) 0.50:0.95 all 100 0.649
Average Recall (AR) 0.50:0.95 small 100 0.526
Average Recall (AR) 0.50:0.95 medium 100 0.744
Average Recall (AR) 0.50:0.95 large 100 0.800

v3 SPP mosaic aug

To train:

python scripts/train_yolo.py --model yolov3_spp --batch_size 4 --epochs 400 --imgs_rect False

To evaluate:

python scripts/eval_yolo.py --model yolov3_spp --batch_size 4 --state_dict <model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.353
Average Precision (AP) 0.50 all 100 0.600
Average Precision (AP) 0.75 all 100 0.360
Average Precision (AP) 0.50:0.95 small 100 0.248
Average Precision (AP) 0.50:0.95 medium 100 0.580
Average Precision (AP) 0.50:0.95 large 100 0.900
Average Recall (AR) 0.50:0.95 all 1 0.194
Average Recall (AR) 0.50:0.95 all 10 0.433
Average Recall (AR) 0.50:0.95 all 100 0.439
Average Recall (AR) 0.50:0.95 small 100 0.336
Average Recall (AR) 0.50:0.95 medium 100 0.659
Average Recall (AR) 0.50:0.95 large 100 0.900

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.594
Average Precision (AP) 0.50 all 100 0.863
Average Precision (AP) 0.75 all 100 0.698
Average Precision (AP) 0.50:0.95 small 100 0.437
Average Precision (AP) 0.50:0.95 medium 100 0.706
Average Precision (AP) 0.50:0.95 large 100 0.769
Average Recall (AR) 0.50:0.95 all 1 0.131
Average Recall (AR) 0.50:0.95 all 10 0.626
Average Recall (AR) 0.50:0.95 all 100 0.668
Average Recall (AR) 0.50:0.95 small 100 0.550
Average Recall (AR) 0.50:0.95 medium 100 0.760
Average Recall (AR) 0.50:0.95 large 100 0.808

framerate: 37.1 fps

v4

To train:

python scripts/train_yolo.py --model yolov4 --batch_size 2 --epochs 400

To evaluate:

python scripts/eval_yolo.py --model yolov4 --batch_size 2 --state_dict <model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.371
Average Precision (AP) 0.50 all 100 0.638
Average Precision (AP) 0.75 all 100 0.408
Average Precision (AP) 0.50:0.95 small 100 0.273
Average Precision (AP) 0.50:0.95 medium 100 0.584
Average Precision (AP) 0.50:0.95 large 100 0.900
Average Recall (AR) 0.50:0.95 all 1 0.206
Average Recall (AR) 0.50:0.95 all 10 0.462
Average Recall (AR) 0.50:0.95 all 100 0.473
Average Recall (AR) 0.50:0.95 small 100 0.372
Average Recall (AR) 0.50:0.95 medium 100 0.689
Average Recall (AR) 0.50:0.95 large 100 0.900

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.566
Average Precision (AP) 0.50 all 100 0.826
Average Precision (AP) 0.75 all 100 0.678
Average Precision (AP) 0.50:0.95 small 100 0.387
Average Precision (AP) 0.50:0.95 medium 100 0.696
Average Precision (AP) 0.50:0.95 large 100 0.743
Average Recall (AR) 0.50:0.95 all 1 0.132
Average Recall (AR) 0.50:0.95 all 10 0.602
Average Recall (AR) 0.50:0.95 all 100 0.643
Average Recall (AR) 0.50:0.95 small 100 0.503
Average Recall (AR) 0.50:0.95 medium 100 0.753
Average Recall (AR) 0.50:0.95 large 100 0.792

framerate: 55 fps

v4 mosaic aug

To train:

python scripts/train_yolo.py --model yolov4 --batch_size 2 --epochs 400 --imgs_rect False

To evaluate:

python scripts/eval_yolo.py --model yolov4 --batch_size 2 --state_dict <model.pth>

On validation set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.392
Average Precision (AP) 0.50 all 100 0.627
Average Precision (AP) 0.75 all 100 0.428
Average Precision (AP) 0.50:0.95 small 100 0.289
Average Precision (AP) 0.50:0.95 medium 100 0.626
Average Precision (AP) 0.50:0.95 large 100 0.900
Average Recall (AR) 0.50:0.95 all 1 0.213
Average Recall (AR) 0.50:0.95 all 10 0.471
Average Recall (AR) 0.50:0.95 all 100 0.477
Average Recall (AR) 0.50:0.95 small 100 0.367
Average Recall (AR) 0.50:0.95 medium 100 0.711
Average Recall (AR) 0.50:0.95 large 100 0.900

On test set:

Metric IoU Thresholds Scales maxDets AP/AR values
Average Precision (AP) 0.50:0.95 all 100 0.606
Average Precision (AP) 0.50 all 100 0.846
Average Precision (AP) 0.75 all 100 0.704
Average Precision (AP) 0.50:0.95 small 100 0.427
Average Precision (AP) 0.50:0.95 medium 100 0.729
Average Precision (AP) 0.50:0.95 large 100 0.787
Average Recall (AR) 0.50:0.95 all 1 0.137
Average Recall (AR) 0.50:0.95 all 10 0.638
Average Recall (AR) 0.50:0.95 all 100 0.680
Average Recall (AR) 0.50:0.95 small 100 0.552
Average Recall (AR) 0.50:0.95 medium 100 0.780
Average Recall (AR) 0.50:0.95 large 100 0.816

framerate: 47.6 fps

Acknowledgements

Repos: MobileNetV2 + Single Shot Multibox Detector and Ultralytics

Project: This work was supported by the PRODUTECH II SIF-POCI-01-0247-FEDER-024541 Project.

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Benchmark for the detection of data matrix landmarks.

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