Dataset Versions
Versions
2024-07-16 8:15am
v45
· 5 months ago
2024-04-18 9:30am
v44
· 8 months ago
2024-02-23 9:49am
v43
· 10 months ago
2023-12-13 9:45am
v42
· a year ago
2023-12-05 11:12am
v41
· a year ago
2023-11-02 11:40am
v40
· a year ago
2023-10-30 1:16pm
v39
· a year ago
2023-09-11 1:50pm
v38
· a year ago
2023-09-08 9:03am
v36
· a year ago
2023-08-05 10:21am
v33
· a year ago
2023-07-22 12:58pm
v32
· a year ago
2023-07-22 12:16pm
v31
· a year ago
2023-07-22 12:12pm
v30
· a year ago
2023-07-14 6:32pm
v29
· a year ago
2023-07-11 9:38am
v28
· a year ago
2023-06-03 1:18pm
v25
· 2 years ago
2023-06-02 9:06pm
v24
· 2 years ago
2023-06-02 7:07pm
v23
· 2 years ago
2023-06-02 6:19pm
v22
· 2 years ago
2023-06-02 1:01pm
v21
· 2 years ago
2023-05-17 8:08am
v20
· 2 years ago
2023-05-16 11:35am
v19
· 2 years ago
2023-05-15 8:13pm
v18
· 2 years ago
2023-05-13 5:38pm
v17
· 2 years ago
2023-05-01 5:21pm
v16
· 2 years ago
2023-03-25 6:07pm
v15
· 2 years ago
2023-03-14 8:15am
v14
· 2 years ago
2023-02-03 5:12pm
v1
· 2 years ago
v30
2023-07-22 12:12pm
Generated on Jul 22, 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.
2491 Total Images
View All Images
Dataset Split
Train Set 88%
2181Images
Valid Set 8%
208Images
Test Set 4%
102Images
Preprocessing
Resize: Fit (reflect edges) in 640x640
Augmentations
Outputs per training example: 3
90° Rotate: Clockwise, Counter-Clockwise
Crop: 0% Minimum Zoom, 20% Maximum Zoom
Rotation: Between -20° and +20°