Dataset Versions
Versions
Original_Dataset
v45
· a year ago
Data with 3X3 Tiling
v44
· a year ago
Data with 3X2 Tiling
v43
· a year ago
Data with 2X3 Tiling
v42
· a year ago
Data with 4X4 Tiling
v41
· a year ago
2024-01-23 2:09pm
v40
· a year ago
Data with 2X2 Tiling
v39
· a year ago
Data without Tiling without Rotation
v38
· a year ago
Data Without Tiling with Rotation
v37
· a year ago
2024-01-23 1:26pm
v36
· a year ago
2024-01-23 1:22pm
v35
· a year ago
2024-01-23 1:19pm
v34
· a year ago
2024-01-22 4:59pm
v33
· a year ago
2024-01-22 4:57pm
v31
· a year ago
2024-01-22 4:57pm
v30
· a year ago
2024-01-22 4:57pm
v29
· a year ago
2024-01-22 4:57pm
v28
· a year ago
2024-01-22 4:56pm
v27
· a year ago
2024-01-22 4:56pm
v26
· a year ago
2024-01-22 4:56pm
v25
· a year ago
2024-01-22 4:50pm
v24
· a year ago
2024-01-16 11:03am
v23
· a year ago
2024-01-09 10:33pm
v22
· a year ago
2024-01-09 6:15pm
v21
· a year ago
2024-01-09 6:14pm
v20
· a year ago
2024-01-09 5:58pm
v19
· a year ago
2024-01-09 5:58pm
v18
· a year ago
2024-01-09 5:57pm
v17
· a year ago
v36
2024-01-23 1:26pm
Generated on Jan 23, 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.
177 Total Images
View All Images
Dataset Split
Train Set 100%
177Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 25% Maximum Zoom
Rotation: Between -14° and +14°
Shear: ±10° Horizontal, ±10° Vertical
Noise: Up to 1.82% of pixels