Dataset Versions
Versions
2024-10-18 6:31am
v28
· a month ago
2024-10-18 6:29am
v27
· a month ago
2024-10-18 6:26am
v26
· a month ago
2024-10-14 5:09am
v25
· a month ago
2024-10-12 6:13am
v24
· a month ago
2024-10-12 3:15am
v23
· 2 months ago
2024-10-11 2:30pm
v22
· 2 months ago
2024-10-11 6:49am
v21
· 2 months ago
2024-10-11 5:34am
v20
· 2 months ago
2024-10-10 8:02am
v19
· 2 months ago
2024-10-10 5:49am
v18
· 2 months ago
2024-10-10 12:51am
v17
· 2 months ago
2024-10-09 10:04am
v16
· 2 months ago
2024-10-09 9:06am
v15
· 2 months ago
2024-10-09 7:06am
v14
· 2 months ago
2024-10-09 5:05am
v13
· 2 months ago
2024-10-08 8:58am
v12
· 2 months ago
2024-10-08 5:26am
v11
· 2 months ago
2024-10-07 5:12pm
v10
· 2 months ago
2024-10-07 11:00am
v9
· 2 months ago
2024-10-07 9:19am
v8
· 2 months ago
2024-10-07 6:03am
v7
· 2 months ago
2024-10-07 4:31am
v6
· 2 months ago
2024-10-03 10:47am
v5
· 2 months ago
2024-10-03 10:33am
v4
· 2 months ago
2024-10-03 5:49am
v3
· 2 months ago
2024-10-01 6:47am
v2
· 2 months ago
2024-09-23 4:52am
v1
· 2 months ago
v5
2024-10-03 10:47am
Generated on Oct 3, 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.
559 Total Images
View All ImagesDataset Split
Train Set 83%
466Images
Valid Set 11%
64Images
Test Set 5%
29Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical