Dataset Versions
Versions
2024-06-11 12:42pm
v25
· 5 months ago
2024-06-11 12:41pm
v24
· 5 months ago
2024-06-11 12:39pm
v23
· 5 months ago
2024-06-11 12:37pm
v22
· 5 months ago
2024-06-11 12:13pm
v21
· 5 months ago
2024-06-11 12:11pm
v20
· 5 months ago
2024-06-06 10:19am
v19
· 6 months ago
2024-06-06 10:17am
v18
· 6 months ago
2024-05-08 11:30am
v17
· 7 months ago
2024-05-08 11:09am
v16
· 7 months ago
2024-05-08 10:49am
v15
· 7 months ago
2024-05-08 10:27am
v14
· 7 months ago
2024-05-08 9:55am
v13
· 7 months ago
2024-04-19 2:20pm
v12
· 7 months ago
2024-04-19 1:50pm
v10
· 7 months ago
2024-04-19 12:57pm
v9
· 7 months ago
2024-04-18 11:17am
v8
· 7 months ago
2024-04-16 1:10pm
v7
· 7 months ago
2024-04-16 1:04pm
v6
· 7 months ago
2024-03-29 10:45am
v5
· 8 months ago
2024-03-28 7:03pm
v4
· 8 months ago
2024-03-28 6:31pm
v3
· 8 months ago
2024-03-28 5:59pm
v2
· 8 months ago
2024-03-26 3:39pm
v1
· 8 months ago
v13
2024-05-08 9:55am
Generated on May 8, 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.
Other Formats
Choose another format.
2597 Total Images
View All Images
Dataset Split
Train Set 97%
2515Images
Valid Set 3%
82Images
Test Set %
0Images
Preprocessing
No preprocessing steps were applied.
Augmentations
Outputs per training example: 3
Flip: Horizontal
Brightness: Between 0% and +15%