Dataset Versions
Versions
2024-11-17 2:59pm
v22
· 13 days ago
2024-11-12 8:08pm
v21
· 18 days ago
2024-11-12 8:07pm
v20
· 18 days ago
2024-10-25 3:53pm
v19
· a month ago
2024-10-25 3:50pm
v18
· a month ago
2024-10-25 3:47pm
v17
· a month ago
2024-10-25 3:47pm
v16
· a month ago
2024-10-25 3:39pm
v15
· a month ago
2024-10-25 3:33pm
v14
· a month ago
2024-10-25 3:32pm
v13
· a month ago
2024-10-25 6:15am
v12
· a month ago
2024-10-25 6:14am
v11
· a month ago
2024-10-24 7:44pm
v10
· a month ago
2024-10-24 7:38pm
v9
· a month ago
2024-10-24 7:36pm
v8
· a month ago
2024-10-23 4:56pm
v7
· a month ago
2024-10-22 7:06pm
v6
· a month ago
2024-10-20 8:51am
v4
· a month ago
2024-10-19 4:26pm
v3
· a month ago
2024-10-17 8:19pm
v2
· a month ago
2024-10-16 6:04am
v1
· a month ago
v21
2024-11-12 8:08pm
Generated on Nov 12, 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.
957 Total Images
View All Images
Dataset Split
Train Set 89%
855Images
Valid Set 7%
68Images
Test Set 4%
34Images
Preprocessing
Auto-Orient: Applied
Static Crop: 17-92% Horizontal Region, 31-100% Vertical Region
Resize: Fit within 640x256
Auto-Adjust Contrast: Using Contrast Stretching
Filter Null: Require at least 85% of images to contain annotations.
Augmentations
Outputs per training example: 3
Rotation: Between -15° and +15°
Brightness: Between -20% and +20%
Blur: Up to 1.2px
Noise: Up to 1.01% of pixels