Dataset Versions
Versions
2023-04-26 11:13am
v41
· 2 years ago
sada
v38
· 2 years ago
2023-01-12 4:23pm
v37
· 2 years ago
2023-01-11 6:14pm
v36
· 2 years ago
2023-01-09 5:50pm
v35
· 2 years ago
2023-01-09 5:21pm
v34
· 2 years ago
2023-01-09 5:21pm
v33
· 2 years ago
2023-01-09 5:21pm
v32
· 2 years ago
2023-01-09 5:21pm
v31
· 2 years ago
2023-01-09 5:21pm
v30
· 2 years ago
2023-01-09 5:21pm
v29
· 2 years ago
2023-01-09 5:21pm
v28
· 2 years ago
2023-01-09 5:21pm
v27
· 2 years ago
2023-01-09 5:20pm
v26
· 2 years ago
2023-01-05 12:45pm
v25
· 2 years ago
2022-09-20 5:13pm
v24
· 2 years ago
2022-09-19 11:42am
v23
· 2 years ago
2022-09-15 12:12pm
v22
· 2 years ago
2022-09-14 4:12pm
v21
· 2 years ago
2022-09-12 5:39pm
v20
· 2 years ago
2022-09-12 5:37pm
v19
· 2 years ago
2022-09-08 3:24pm
v18
· 2 years ago
2022-09-02 5:17pm
v17
· 2 years ago
2022-09-02 4:53pm
v16
· 2 years ago
2022-09-02 4:34pm
v15
· 2 years ago
2022-09-01 5:33pm
v14
· 2 years ago
2022-07-18 12:33pm
v13
· 2 years ago
2022-07-16 12:58pm
v12
· 2 years ago
2022-07-16 12:14pm
v11
· 2 years ago
2022-07-15 9:07pm
v10
· 2 years ago
v25
2023-01-05 12:45pm
Generated on Jan 5, 2023
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.
410 Total Images
View All Images
Dataset Split
Train Set 88%
360Images
Valid Set 12%
50Images
Test Set %
0Images
Preprocessing
Modify Classes: 2 remapped, 1 dropped
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: ±15° Horizontal, ±15° Vertical
Exposure: Between -25% and +25%
Blur: Up to 10px
Noise: Up to 10% of pixels
Cutout: 3 boxes with 10% size each
Bounding Box: 90° Rotate: Clockwise, Counter-Clockwise
Bounding Box: Rotation: Between -15° and +15°
Bounding Box: Shear: ±15° Horizontal, ±15° Vertical
Bounding Box: Noise: Up to 5% of pixels