Dataset Versions
Versions
2024-12-20 2:29pm
v31
· 2 days ago
2024-11-22 11:05pm
v30
· a month ago
2024-11-22 10:48pm
v29
· a month ago
2024-11-15 10:47am
v28
· a month ago
2024-11-13 12:58am
v27
· a month ago
2024-11-12 10:24pm
v26
· a month ago
2024-11-12 8:49pm
v25
· a month ago
2024-11-12 8:17pm
v24
· a month ago
2024-10-25 10:15pm
v23
· 2 months ago
2024-10-17 4:46am
v22
· 2 months ago
2024-10-10 5:56pm
v21
· 2 months ago
2024-10-08 7:26pm
v20
· 2 months ago
2024-09-17 9:32pm
v17
· 3 months ago
2024-09-10 8:34pm
v16
· 3 months ago
2024-07-09 5:06pm
v15
· 5 months ago
2024-03-20 5:01pm
v14
· 9 months ago
2024-02-27 6:34pm
v13
· 10 months ago
2024-02-27 6:22pm
v12
· 10 months ago
2024-01-31 10:38am
v11
· a year ago
2024-01-28 1:35pm
v10
· a year ago
2023-07-27 4:04pm
v9
· a year ago
10m-exp3-tiles-combo
v8
· a year ago
10m-exp2-tiles
v7
· a year ago
10m-class-combination
v6
· a year ago
10m-exp1-singles
v5
· a year ago
2023-07-11 3:54pm
v4
· a year ago
2023-07-11 9:35am
v3
· a year ago
2023-06-27 4:40pm
v2
· a year ago
2023-06-27 8:53am
v1
· a year ago
v9
2023-07-27 4:04pm
Generated on Jul 27, 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.
2040 Total Images
View All Images
Dataset Split
Train Set 87%
1770Images
Valid Set 5%
97Images
Test Set 8%
173Images
Preprocessing
Auto-Orient: Applied
Tile: 2 rows x 2 columns
Modify Classes: 2 remapped, 0 dropped
Filter Null: Require all images to contain annotations.
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
Bounding Box: 90° Rotate: Clockwise, Counter-Clockwise, Upside Down