Dataset Versions
Versions
2022-08-16 10:14pm
v30
· 2 years ago
2022-08-16 10:13pm
v29
· 2 years ago
2022-08-16 10:12pm
v28
· 2 years ago
2022-08-16 10:12pm
v27
· 2 years ago
2022-08-16 10:11pm
v26
· 2 years ago
2022-08-16 10:10pm
v25
· 2 years ago
2022-08-16 10:09pm
v24
· 2 years ago
2022-08-16 10:09pm
v23
· 2 years ago
2022-08-16 10:08pm
v22
· 2 years ago
2022-08-16 10:06pm
v21
· 2 years ago
2022-08-16 10:00pm
v20
· 2 years ago
2022-08-16 9:59pm
v19
· 2 years ago
2022-08-16 9:58pm
v18
· 2 years ago
2022-08-16 9:58pm
v17
· 2 years ago
2022-08-16 9:57pm
v16
· 2 years ago
2022-08-16 9:56pm
v15
· 2 years ago
2022-08-16 9:56pm
v14
· 2 years ago
2022-08-16 9:55pm
v13
· 2 years ago
2022-08-16 9:55pm
v12
· 2 years ago
2022-08-16 9:54pm
v11
· 2 years ago
2022-08-16 9:53pm
v10
· 2 years ago
2022-08-16 9:51pm
v9
· 2 years ago
2022-08-16 9:49pm
v8
· 2 years ago
2022-08-16 9:49pm
v7
· 2 years ago
2022-08-16 9:48pm
v6
· 2 years ago
2022-08-16 9:47pm
v5
· 2 years ago
2022-08-16 9:46pm
v4
· 2 years ago
2022-08-16 9:46pm
v3
· 2 years ago
2022-08-16 9:43pm
v2
· 2 years ago
2022-08-16 9:42pm
v1
· 2 years ago
v26
2022-08-16 10:11pm
Generated on Aug 16, 2022
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.
108 Total Images
View All Images
Dataset Split
Train Set 100%
108Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -15° and +15°
Shear: ±15° Horizontal, ±15° Vertical
Brightness: Between -25% and +25%
Noise: Up to 3% of pixels