Dataset Versions
Versions
2024-11-07 6:45pm
v25
· a month ago
2024-10-29 1:18pm
v24
· 2 months ago
2024-10-25 8:14am
v23
· 2 months ago
2024-10-08 7:43pm
v22
· 2 months ago
2024-09-24 1:33pm
v21
· 3 months ago
2024-09-10 3:35pm
v20
· 3 months ago
2024-08-15 9:17am
v19
· 4 months ago
2024-07-23 2:12pm
v18
· 5 months ago
2024-07-12 1:49pm
v17
· 5 months ago
2024-07-12 1:41pm
v16
· 5 months ago
2024-07-12 1:38pm
v15
· 5 months ago
2024-07-12 10:01am
v14
· 5 months ago
2024-07-02 12:35pm
v13
· 6 months ago
2024-06-28 7:54am
v12
· 6 months ago
2024-06-12 10:00pm
v11
· 6 months ago
2024-05-28 8:14pm
v10
· 7 months ago
2024-05-25 5:39pm
v9
· 7 months ago
2024-05-07 10:24am
v8
· 7 months ago
2024-05-04 1:58pm
v7
· 7 months ago
2024-05-04 1:48pm
v6
· 7 months ago
2024-05-03 4:56pm
v5
· 7 months ago
2024-05-03 10:42am
v4
· 7 months ago
2024-05-03 10:00am
v3
· 7 months ago
2024-05-03 9:29am
v2
· 7 months ago
2024-05-02 4:47pm
v1
· 8 months ago
v24
2024-10-29 1:18pm
Generated on Oct 29, 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.
10012 Total Images
View All Images
Dataset Split
Train Set 82%
8250Images
Valid Set 12%
1177Images
Test Set 6%
585Images
Preprocessing
Auto-Orient: Applied
Static Crop: 25-75% Horizontal Region, 25-75% Vertical Region
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 2
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -15° and +15°
Saturation: Between -25% and +25%
Exposure: Between -10% and +10%
Noise: Up to 0.1% of pixels