Dataset Versions
Versions
2021-11-07 4:07pm
v37
· 3 years ago
2021-10-25 11:45pm
v36
· 3 years ago
2021-10-25 8:58am
v35
· 3 years ago
2021-10-24 7:50pm
v31
· 3 years ago
2021-10-24 6:30pm
v29
· 3 years ago
2021-10-24 5:43pm
v28
· 3 years ago
2021-10-24 5:39pm
v27
· 3 years ago
2021-10-24 5:37pm
v26
· 3 years ago
2021-10-24 3:08pm
v25
· 3 years ago
2021-10-24 2:44pm
v24
· 3 years ago
2021-10-24 1:24pm
v23
· 3 years ago
2021-10-24 12:02pm
v22
· 3 years ago
2021-10-24 10:19am
v21
· 3 years ago
2021-10-23 11:14pm
v19
· 3 years ago
2021-10-23 9:33pm
v18
· 3 years ago
2021-10-23 8:02pm
v17
· 3 years ago
2021-10-23 6:26pm
v16
· 3 years ago
2021-10-23 6:23pm
v15
· 3 years ago
2021-10-23 4:33pm
v14
· 3 years ago
2021-10-21 8:45am
v13
· 3 years ago
2021-10-17 9:14pm
v11
· 3 years ago
2021-10-17 6:21pm
v10
· 3 years ago
2021-10-17 2:08pm
v8
· 3 years ago
2021-10-10 6:43pm
v6
· 3 years ago
v29
2021-10-24 6:30pm
Generated on Oct 24, 2021
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.
1254 Total Images
View All Images
Dataset Split
Train Set 95%
1197Images
Valid Set 4%
50Images
Test Set 1%
7Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 416x416
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -45° and +45°
Hue: Between -26° and +26°
Saturation: Between -45% and +45%
Brightness: Between -46% and +46%
Exposure: Between -30% and +30%
Bounding Box: Flip: Horizontal, Vertical
Bounding Box: 90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Bounding Box: Brightness: Between -15% and +15%
Bounding Box: Exposure: Between -15% and +15%