Dataset Versions
Versions
2022-01-07 10:28am
v27
· 3 years ago
raw dataset- no null annotations
v24
· 3 years ago
final dataset blur and exposure no grey
v23
· 3 years ago
final dataset only exposure
v22
· 3 years ago
final dataset only saturation
v21
· 3 years ago
final dataset blur and sat no grey
v20
· 3 years ago
BEST- final dataset with blur and brightness
v19
· 3 years ago
final dataset with blur and saturation
v18
· 3 years ago
2. final dataset with blur and noise -2--
v17
· 3 years ago
final dataset with blur 2px
v16
· 3 years ago
final dataset with blur 4px
v15
· 3 years ago
final dataset with exposure
v14
· 3 years ago
final dataset with saturation
v13
· 3 years ago
final dataset with blur and noise
v12
· 3 years ago
final dataset with blur
v11
· 3 years ago
final dataset with greyscale and hue
v10
· 3 years ago
final dataset with greyscale- hue and noise
v9
· 3 years ago
final dataset with greyscale and noise
v8
· 3 years ago
-80-10-10- final dataset- no greyscale- shear or rotate
v6
· 3 years ago
final dataset- no shear- rotate or greyscale
v2
· 3 years ago
final dataset- no shear or rotate
v1
· 3 years ago
v21
final dataset only saturation
Generated on Jan 5, 2022
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.
1297 Total Images
View All Images
Dataset Split
Train Set 87%
1125Images
Valid Set 9%
111Images
Test Set 5%
61Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 416x416
Filter Null: Require all images to contain annotations.
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise
Crop: 0% Minimum Zoom, 20% Maximum Zoom
Saturation: Between -25% and +25%