Dataset Versions
Versions
60-20-20-aug2-b5
v46
· 2 months ago
60-20-20-aug2-b4
v45
· 2 months ago
60-20-20-aug2-b3
v44
· 2 months ago
60-20-20-aug2-b2
v43
· 2 months ago
60-20-20-aug2-b1
v42
· 2 months ago
70-15-15-aug2-b5
v41
· 2 months ago
70-15-15-aug2-b4
v40
· 2 months ago
70-15-15-aug2-b3
v39
· 2 months ago
70-15-15-aug2-b2
v38
· 2 months ago
70-15-15-aug2-b1
v37
· 2 months ago
80-10-10-aug2-b5
v36
· 2 months ago
80-10-10-aug2-b4
v35
· 2 months ago
80-10-10-aug2-b3
v34
· 2 months ago
80-10-10-aug2-b2
v33
· 2 months ago
80-10-10-aug2-b1
v32
· 2 months ago
85-7-7-aug2-b5
v31
· 2 months ago
85-7-7-aug2-b4
v30
· 2 months ago
85-7-7-aug2-b3
v29
· 2 months ago
85-7-7-aug2-b2
v28
· 2 months ago
85-7-7-aug2-b1
v27
· 2 months ago
90-5-5-aug1
v14
· 2 months ago
90-5-5-noaug
v13
· 2 months ago
85-7-7-noaug
v12
· 2 months ago
80-10-10-noaug
v11
· 2 months ago
70-15-15-noaug
v10
· 2 months ago
60-20-20-noaug
v9
· 2 months ago
85-7-7-aug
v8
· 2 months ago
80-10-10-aug1
v7
· 2 months ago
70-15-15-aug1
v6
· 2 months ago
60-20-20-aug1
v5
· 2 months ago
Raw-100Train-NoAug
v1
· 2 months ago
v14
90-5-5-aug1
Generated on Oct 24, 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.
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1995 Total Images
View All Images
Dataset Split
Train Set 90%
1797Images
Valid Set 5%
99Images
Test Set 5%
99Images
Preprocessing
No preprocessing steps were applied.
Augmentations
Outputs per training example: 3
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -15° and +15°
Shear: ±10° Horizontal, ±15° Vertical
Saturation: Between -35% and +35%
Brightness: Between -20% and +20%
Exposure: Between -15% and +15%