Dataset Versions
Versions
2024-03-17 8:22pm
v41
· 8 months ago
2024-03-16 12:37pm
v40
· 8 months ago
2024-03-15 1:45pm
v38
· 8 months ago
2024-03-12 4:57pm
v37
· 8 months ago
2024-03-11 6:48pm
v36
· 8 months ago
2024-03-09 9:35pm
v35
· 8 months ago
2024-03-08 11:02pm
v34
· 8 months ago
2024-03-08 5:06pm
v33
· 8 months ago
2024-03-07 3:07pm
v32
· 8 months ago
2024-03-06 5:37pm
v31
· 9 months ago
2024-03-03 4:31pm
v30
· 9 months ago
2024-03-03 4:30pm
v29
· 9 months ago
2024-03-02 4:32pm
v28
· 9 months ago
2024-03-01 4:32pm
v27
· 9 months ago
2024-02-28 5:27pm
v20
· 9 months ago
2024-02-27 5:33pm
v18
· 9 months ago
2024-02-26 3:52pm
v16
· 9 months ago
2024-02-24 7:00pm
v14
· 9 months ago
2024-02-24 5:46pm
v13
· 9 months ago
2024-02-24 5:11pm
v12
· 9 months ago
2024-02-24 3:23pm
v11
· 9 months ago
2024-02-22 3:55pm
v4
· 9 months ago
2024-02-22 2:10pm
v3
· 9 months ago
2024-02-21 9:34pm
v2
· 9 months ago
v14
2024-02-24 7:00pm
Generated on Feb 24, 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.
349 Total Images
View All Images
Dataset Split
Train Set 88%
307Images
Valid Set 8%
29Images
Test Set 4%
13Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 3
Brightness: Between -15% and +15%