Dataset Versions
Versions
allx3_2No-Contrast
v43
· a year ago
allx3_1No-Contrast
v42
· a year ago
allx3_2Contrast
v41
· a year ago
allx3_1Contrast
v40
· a year ago
320x320 logi-2.9k
v39
· a year ago
640x640 logi-2.9k
v38
· a year ago
320x320 logi-1.9k
v35
· a year ago
416x416 logi-2.9k
v34
· a year ago
416x416 logi-1.9k
v33
· a year ago
640x640 logi-1.9k
v31
· a year ago
16overlap-2023-05-21
v28
· 2 years ago
15overlap-2023-05-20
v27
· 2 years ago
14empty-2023-03-27
v26
· 2 years ago
13empty-2023-03-27
v25
· 2 years ago
p12-2023-03-22 8-18pm
v24
· 2 years ago
p11-2023-03-22 8-17pm
v23
· 2 years ago
p10-2023-03-22 1-00pm
v22
· 2 years ago
2023-03-22 12:56pm
v21
· 2 years ago
p8-2023-03-22 12-52pm
v20
· 2 years ago
p7-2023-03-22 12-50pm
v19
· 2 years ago
p062023-03-22 12-45pm
v18
· 2 years ago
p052023-03-22 12-43pm
v17
· 2 years ago
p03-2023-03-20 12-49am
v16
· 2 years ago
v18
p062023-03-22 12-45pm
Generated on Mar 22, 2023
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TXT annotations and YAML config used with YOLOv11.
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TXT annotations and YAML config used with YOLOv8.
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TXT annotations and YAML config used with YOLOv5.
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TXT annotations and YAML config used with YOLOv7.
COCO JSON
COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.
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Darknet TXT annotations used with YOLO Darknet (both v3 and v4) and YOLOv3 PyTorch.
Pascal VOC XML
Common XML annotation format for local data munging (pioneered by ImageNet).
TFRecord
TFRecord binary format used for both Tensorflow 1.5 and Tensorflow 2.0 Object Detection models.
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900 Total Images
View All Images
Dataset Split
Train Set 100%
900Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Resize: Fit within 640x640
Auto-Adjust Contrast: Using Contrast Stretching
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -5° and +5°
Shear: ±10° Horizontal, ±19° Vertical
Hue: Between -29° and +29°
Saturation: Between -70% and +70%
Brightness: Between -40% and +40%
Blur: Up to 2.5px
Noise: Up to 3% of pixels
Cutout: 25 boxes with 1% size each