Dataset Versions
Versions
2025-01-18 2:44pm
v24
· a month ago
2025-01-18 2:39pm
v23
· a month ago
2024-12-11 6:15pm
v22
· 2 months ago
2024-12-04 3:19pm
v21
· 2 months ago
2024-11-13 10:37am
v20
· 3 months ago
2024-11-13 10:25am
v19
· 3 months ago
2024-11-13 6:21am
v18
· 3 months ago
2024-11-13 5:27am
v17
· 3 months ago
2024-11-11 11:08am
v16
· 3 months ago
2024-11-11 9:20am
v15
· 3 months ago
2024-10-26 5:52am
v14
· 4 months ago
2024-10-26 5:48am
v13
· 4 months ago
2024-10-26 5:04am
v12
· 4 months ago
2024-10-26 4:51am
v11
· 4 months ago
2024-10-25 5:06am
v10
· 4 months ago
2024-10-19 9:52am
v9
· 4 months ago
2024-10-19 5:57am
v8
· 4 months ago
2024-10-18 5:55am
v7
· 4 months ago
2024-10-18 5:51am
v6
· 4 months ago
2024-10-18 5:17am
v5
· 4 months ago
2024-10-18 5:12am
v4
· 4 months ago
2024-10-08 8:24am
v3
· 4 months ago
2024-10-07 10:30am
v2
· 4 months ago
2024-10-07 10:27am
v1
· 4 months ago
v16
2024-11-11 11:08am
Generated on Nov 11, 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.
4005 Total Images
View All ImagesDataset Split
Train Set 97%
3899Images
Valid Set 3%
102Images
Test Set 0%
4Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 7
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 30% Maximum Zoom
Rotation: Between -45° and +45°
Shear: ±11° Horizontal, ±10° Vertical
Hue: Between -4° and +4°
Saturation: Between -5% and +5%
Brightness: Between -5% and +5%