Dataset Versions
Versions
2024-07-09 2:45am
v29
· 6 months ago
2024-06-26 7:04am
v28
· 6 months ago
2024-06-24 8:40am
v27
· 6 months ago
2024-06-24 1:58am
v26
· 6 months ago
2024-06-07 5:04pm
v25
· 7 months ago
2024-06-07 12:01pm
v24
· 7 months ago
2024-05-30 3:33pm
v23
· 7 months ago
2024-05-28 11:11am
v22
· 7 months ago
2024-05-23 2:25pm
v21
· 7 months ago
2024-05-21 2:51pm
v20
· 7 months ago
2024-05-21 2:50pm
v19
· 7 months ago
2024-05-21 2:45pm
v18
· 7 months ago
2024-04-26 3:47pm
v17
· 8 months ago
2024-04-25 5:15pm
v16
· 8 months ago
2024-04-24 3:22pm
v15
· 8 months ago
2024-04-24 3:21pm
v14
· 8 months ago
2024-04-23 2:19pm
v13
· 8 months ago
2024-04-23 11:17am
v12
· 8 months ago
2024-04-17 12:15pm
v11
· 8 months ago
2024-04-17 11:12am
v7
· 8 months ago
2024-04-17 10:06am
v6
· 8 months ago
2024-04-15 4:55pm
v5
· 9 months ago
2024-04-12 5:27pm
v4
· 9 months ago
2024-04-12 12:13pm
v3
· 9 months ago
v6
2024-04-17 10:06am
Generated on Apr 17, 2024
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.
Other Formats
Choose another format.
71 Total Images
View All Images
Dataset Split
Train Set 89%
63Images
Valid Set 7%
5Images
Test Set 4%
3Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 512x512
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
Brightness: Between -10% and +10%
Exposure: Between -10% and +10%
Blur: Up to 1.2px
Noise: Up to 0.3% of pixels
Cutout: 4 boxes with 15% size each