Dataset Versions
Versions
2023-06-01 6:29pm
v31
· a year ago
2023-06-01 6:29pm
v30
· a year ago
2023-06-01 6:29pm
v29
· a year ago
2023-06-01 6:29pm
v28
· a year ago
2023-06-01 6:29pm
v27
· a year ago
2023-06-01 6:29pm
v26
· a year ago
2023-06-01 6:29pm
v25
· a year ago
2023-06-01 6:29pm
v24
· a year ago
2023-06-01 6:29pm
v23
· a year ago
2023-06-01 6:27pm
v22
· a year ago
2023-04-12 11:45pm
v21
· 2 years ago
2023-04-12 10:55pm
v20
· 2 years ago
2023-04-12 10:46pm
v19
· 2 years ago
2023-04-12 10:32pm
v18
· 2 years ago
2023-04-12 9:44pm
v17
· 2 years ago
550
v16
· 2 years ago
2023-04-12 6:20pm
v15
· 2 years ago
2023-04-12 6:18pm
v14
· 2 years ago
2023-04-12 6:16pm
v13
· 2 years ago
2023-04-12 6:04pm
v12
· 2 years ago
2023-04-12 6:02pm
v11
· 2 years ago
2023-04-12 6:00pm
v10
· 2 years ago
2023-04-12 5:59pm
v9
· 2 years ago
2023-04-12 5:58pm
v8
· 2 years ago
2023-04-12 4:51pm
v7
· 2 years ago
2023-04-12 4:43pm
v6
· 2 years ago
2023-04-12 4:23pm
v5
· 2 years ago
2023-04-12 4:18pm
v4
· 2 years ago
2023-04-12 4:15pm
v3
· 2 years ago
2023-04-12 3:37pm
v2
· 2 years ago
2023-04-12 3:34pm
v1
· 2 years ago
v17
2023-04-12 9:44pm
Generated on Apr 12, 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.
275 Total Images
View All Images
Dataset Split
Train Set 76%
210Images
Valid Set 13%
37Images
Test Set 10%
28Images
Preprocessing
Auto-Orient: Applied
Static Crop: 25-75% Horizontal Region, 25-75% Vertical Region
Augmentations
Outputs per training example: 3
Flip: Horizontal
90° Rotate: Clockwise, Counter-Clockwise
Crop: 0% Minimum Zoom, 31% Maximum Zoom
Rotation: Between -15° and +15°
Grayscale: Apply to 29% of images
Saturation: Between -27% and +27%
Brightness: Between -25% and +25%
Exposure: Between -22% and +22%
Blur: Up to 10px
Noise: Up to 10% of pixels
Bounding Box: 90° Rotate: Clockwise, Counter-Clockwise
Bounding Box: Crop: 0% Minimum Zoom, 52% Maximum Zoom
Bounding Box: Rotation: Between -45° and +45°
Bounding Box: Shear: ±27° Horizontal, ±30° Vertical