Dataset Versions
Versions
2023-02-18 6:02pm
v27
· 2 years ago
2023-02-17 7:32pm
v26
· 2 years ago
2023-02-17 7:22pm
v25
· 2 years ago
2023-02-17 3:02pm
v24
· 2 years ago
2023-02-15 7:57pm
v23
· 2 years ago
2023-02-15 7:54pm
v22
· 2 years ago
2023-02-13 12:50pm
v21
· 2 years ago
2023-02-12 9:40am
v20
· 2 years ago
2023-02-11 11:37pm
v19
· 2 years ago
2023-02-11 1:22am
v18
· 2 years ago
2023-02-10 11:54pm
v17
· 2 years ago
2023-02-10 11:17pm
v16
· 2 years ago
2023-02-10 8:27pm
v15
· 2 years ago
2023-02-09 2:50pm
v14
· 2 years ago
2023-02-09 2:47pm
v13
· 2 years ago
2023-02-09 2:10pm
v12
· 2 years ago
2023-02-09 2:05pm
v11
· 2 years ago
2023-02-09 1:52pm
v10
· 2 years ago
2023-02-09 1:48pm
v9
· 2 years ago
2023-02-09 9:58am
v8
· 2 years ago
2023-02-09 8:54am
v7
· 2 years ago
2023-02-09 1:49am
v6
· 2 years ago
2023-02-09 12:28am
v5
· 2 years ago
2023-02-08 7:34pm
v4
· 2 years ago
2023-02-08 7:29pm
v3
· 2 years ago
2023-02-08 7:26pm
v2
· 2 years ago
2023-02-08 3:19pm
v1
· 2 years ago
v7
2023-02-09 8:54am
Generated on Feb 9, 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.
158 Total Images
View All Images
Dataset Split
Train Set 85%
135Images
Valid Set 8%
12Images
Test Set 7%
11Images
Preprocessing
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 3
Cutout: 3 boxes with 10% size each