Dataset Versions
Versions
2024-12-21 2:09am
v30
· 2 days ago
2024-12-21 2:07am
v29
· 2 days ago
2024-12-21 2:04am
v28
· 2 days ago
2024-12-21 2:01am
v27
· 2 days ago
2024-12-21 1:57am
v26
· 2 days ago
2024-12-21 1:54am
v25
· 2 days ago
2024-12-21 1:50am
v24
· 2 days ago
2024-12-21 1:46am
v23
· 2 days ago
2024-12-21 1:38am
v22
· 2 days ago
2024-12-20 11:08pm
v21
· 2 days ago
2024-12-20 10:57pm
v20
· 2 days ago
2024-12-20 10:51pm
v19
· 2 days ago
2024-12-20 10:43pm
v18
· 2 days ago
2024-12-20 10:31pm
v17
· 3 days ago
2024-12-20 10:19pm
v16
· 3 days ago
2024-12-20 10:15pm
v15
· 3 days ago
2024-12-20 10:07pm
v14
· 3 days ago
2024-12-20 10:00pm
v13
· 3 days ago
2024-12-20 9:30pm
v12
· 3 days ago
2024-12-20 9:30pm
v11
· 3 days ago
2024-12-12 3:07pm
v10
· 11 days ago
2024-12-12 3:01pm
v9
· 11 days ago
2024-12-12 3:00pm
v8
· 11 days ago
2024-12-12 3:00pm
v7
· 11 days ago
2024-12-12 3:00pm
v6
· 11 days ago
2024-12-12 3:00pm
v5
· 11 days ago
2024-12-12 3:00pm
v4
· 11 days ago
2024-12-12 3:00pm
v3
· 11 days ago
2024-12-12 3:00pm
v2
· 11 days ago
2024-12-12 3:00pm
v1
· 11 days ago
v15
2024-12-20 10:15pm
Generated on Dec 20, 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.
480 Total Images
View All Images
Dataset Split
Train Set 88%
420Images
Valid Set 8%
40Images
Test Set 4%
20Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 224x224
Augmentations
Outputs per training example: 3
Crop: 0% Minimum Zoom, 33% Maximum Zoom
Rotation: Between -15° and +15°
Shear: ±15° Horizontal, ±15° Vertical
Grayscale: Apply to 33% of images
Hue: Between -60° and +60°
Saturation: Between -33% and +33%
Brightness: Between -33% and +33%
Exposure: Between -33% and +33%
Blur: Up to 8.1px
Noise: Up to 3.33% of pixels
Cutout: 6 boxes with 15% size each