Dataset Versions
Versions
2024-07-31 3:34am
v9
· 5 months ago
2024-07-30 9:58am
v8
· 5 months ago
2024-07-30 9:34am
v7
· 5 months ago
2024-07-30 9:14am
v6
· 5 months ago
2024-07-30 4:08am
v5
· 5 months ago
2024-07-22 6:38am
v4
· 5 months ago
2024-07-19 8:35am
v3
· 5 months ago
2024-07-18 8:50am
v2
· 5 months ago
2024-07-18 8:40am
v1
· 5 months ago
v7
2024-07-30 9:34am
Generated on Jul 30, 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.
2592 Total Images
View All Images
Dataset Split
Train Set 87%
2256Images
Valid Set 9%
228Images
Test Set 4%
108Images
Preprocessing
Auto-Orient: Applied
Static Crop: 25-75% Horizontal Region, 25-75% Vertical Region
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Tile: 2 rows x 2 columns
Augmentations
Outputs per training example: 3
Flip: Horizontal
90° Rotate: Clockwise, Counter-Clockwise
Crop: 0% Minimum Zoom, 20% Maximum Zoom
Rotation: Between -15° and +15°
Shear: ±10° Horizontal, ±10° Vertical
Hue: Between -15° and +15°
Saturation: Between -25% and +25%
Brightness: Between -49% and +49%
Exposure: Between -10% and +10%
Blur: Up to 2.3px
Noise: Up to 10% of pixels
Cutout: 3 boxes with 10% size each
Mosaic: Applied