Dataset Versions
Versions
2024-10-29 5:35pm
v56
· a month ago
2024-10-29 5:32pm
v55
· a month ago
2024-10-28 12:53pm
v54
· a month ago
2024-10-28 12:22pm
v53
· a month ago
2024-10-28 11:22am
v52
· a month ago
2024-10-27 10:11pm
v50
· a month ago
2024-10-27 9:21pm
v49
· a month ago
2024-10-27 7:23pm
v48
· a month ago
2024-10-27 7:06pm
v47
· a month ago
2024-10-27 4:23pm
v46
· a month ago
2024-10-27 4:16pm
v44
· a month ago
2024-10-27 4:10pm
v43
· a month ago
2024-10-27 9:32am
v42
· a month ago
2024-10-26 9:25pm
v31
· a month ago
2024-10-23 2:59pm
v25
· a month ago
2024-10-21 11:11pm
v24
· a month ago
2024-05-30 10:42pm
v23
· 6 months ago
2024-05-30 10:09pm
v22
· 6 months ago
2023-11-25 2:15am
v21
· a year ago
2023-11-25 2:15am
v20
· a year ago
2023-11-25 2:06am
v19
· a year ago
2023-11-25 12:48am
v11
· a year ago
2023-11-09 12:48am
v7
· a year ago
2023-10-21 9:53pm
v4
· a year ago
2023-10-15 2:47am
v3
· a year ago
v11
2023-11-25 12:48am
Generated on Nov 24, 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.
Other Formats
Choose another format.
73 Total Images
View All Images
Dataset Split
Train Set 86%
63Images
Valid Set 10%
7Images
Test Set 4%
3Images
Preprocessing
Auto-Orient: Applied
Resize: Fit within 480x480
Augmentations
Outputs per training example: 3
Shear: ±15° Horizontal, ±15° Vertical
Brightness: Between -25% and +25%
Exposure: Between -25% and +25%