Dataset Versions
Versions
2024-11-27 5:36pm
v55
· 3 days ago
2024-11-27 3:41pm
v53
· 4 days ago
2024-11-27 2:16pm
v52
· 4 days ago
2024-11-27 11:10am
v51
· 4 days ago
2024-11-20 10:38pm
v50
· 10 days ago
2024-11-18 4:05pm
v49
· 13 days ago
2024-11-18 4:04pm
v48
· 13 days ago
2024-11-18 4:04pm
v47
· 13 days ago
2024-11-18 4:03pm
v46
· 13 days ago
2024-08-23 9:49am
v45
· 3 months ago
2024-08-22 4:40pm
v43
· 3 months ago
line_stickok
v42
· 3 months ago
line_stickv1
v41
· 3 months ago
line_stickv3
v35
· 3 months ago
line_stickv2
v33
· 5 months ago
line_ColorV3
v30
· 7 months ago
line_ColorV2
v25
· 7 months ago
leakingBronzeAndMissingZN
v19
· a year ago
line_stick_package
v18
· a year ago
Line_color_augment
v17
· a year ago
Line_color
v13
· a year ago
leakingBronze
v6
· a year ago
2023-09-16 9:32am
v5
· a year ago
2023-09-14 9:28am
v4
· a year ago
2023-09-07 7:42pm
v3
· a year ago
2023-08-30 10:31am
v2
· a year ago
v17
Line_color_augment
Generated on Nov 7, 2023
Popular Download Formats
YOLOv11
TXT annotations and YAML config used with YOLOv11.
YOLOv9
TXT annotations and YAML config used with YOLOv9.
YOLOv8
TXT annotations and YAML config used with YOLOv8.
YOLOv5
TXT annotations and YAML config used with YOLOv5.
YOLOv7
TXT annotations and YAML config used with YOLOv7.
COCO JSON
COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.
YOLO Darknet
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.
PaliGemma
PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.
CreateML JSON
CreateML JSON format is used with Apple's CreateML and Turi Create tools.
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56 Total Images
View All Images
Dataset Split
Train Set 96%
54Images
Valid Set 2%
1Images
Test Set 2%
1Images
Preprocessing
Auto-Orient: Applied
Modify Classes: 8 remapped, 7 dropped
Filter by Tag: 1 required, 1 dropped
Augmentations
Outputs per training example: 3
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -45° and +45°
Brightness: Between -20% and +20%
Noise: Up to 10% of pixels
Mosaic: Applied
Bounding Box: Flip: Horizontal, Vertical
Bounding Box: Shear: ±15° Horizontal, ±15° Vertical
Bounding Box: Exposure: Between -25% and +25%