Dataset Versions
Versions
2024-07-15 9-35am w- cutouts
v26
· 4 months ago
2024-07-15 9-23am w- cutouts
v25
· 4 months ago
2024-07-15 9:18am
v24
· 4 months ago
2024-07-15 9:11am
v23
· 4 months ago
2024-07-15 8:31am
v22
· 4 months ago
2024-07-15 7:55am
v21
· 4 months ago
2024-07-15 7:45am
v20
· 4 months ago
2024-07-15 5:47am
v19
· 4 months ago
2024-07-14 11:43am
v18
· 4 months ago
2024-07-14 8-00am-simple box-
v17
· 4 months ago
2024-07-14 7:42am
v16
· 4 months ago
2024-07-12 9:59am
v14
· 4 months ago
2024-07-12 7:30am
v13
· 4 months ago
2024-07-12 6:25am
v12
· 4 months ago
2024-07-12 5:41am
v11
· 4 months ago
2024-07-11 6:57pm
v10
· 4 months ago
2024-07-11 5:51pm
v9
· 4 months ago
2024-07-11 5:22pm
v8
· 4 months ago
2024-07-11 12:41pm
v7
· 4 months ago
2024-07-11 12:20pm
v6
· 4 months ago
2024-07-11 12:16pm
v5
· 4 months ago
2024-07-11 10:40am
v4
· 4 months ago
2024-07-11 8:04am
v3
· 4 months ago
2024-07-11 7:51am
v2
· 4 months ago
2024-07-11 7:12am
v1
· 4 months ago
v19
2024-07-15 5:47am
Generated on Jul 15, 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.
3839 Total Images
View All Images
Dataset Split
Train Set 88%
3360Images
Valid Set 8%
319Images
Test Set 4%
160Images
Preprocessing
Auto-Orient: Applied
Resize: Fit within 640x640
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 22% Maximum Zoom
Rotation: Between -15° and +15°
Shear: ±10° Horizontal, ±10° Vertical
Grayscale: Apply to 12% of images
Hue: Between -24° and +24°
Saturation: Between -34% and +34%
Brightness: Between -23% and +23%
Exposure: Between -15% and +15%
Blur: Up to 0.6px
Noise: Up to 1.56% of pixels