Dataset Versions
Versions
2024-11-13 10:11am
v26
· 3 days ago
2024-11-13 8:17am
v25
· 4 days ago
2024-11-12 7:15am
v24
· 5 days ago
2024-11-11 5:00pm
v23
· 5 days ago
2024-11-01 2:07pm
v22
· 15 days ago
2024-10-31 8:15pm
v21
· 16 days ago
2024-10-31 8:04pm
v20
· 16 days ago
2024-10-27 12:46pm
v19
· 20 days ago
2024-10-26 7:51am
v18
· 22 days ago
2024-10-25 5:33pm
v17
· 22 days ago
2024-10-16 3:02pm
v16
· a month ago
2024-10-16 2:56pm
v15
· a month ago
2024-10-16 8:00am
v14
· a month ago
2024-10-10 9:27pm
v13
· a month ago
2024-10-10 1:25pm
v12
· a month ago
2024-10-06 12:19pm
v11
· a month ago
2024-10-05 8:57pm
v10
· a month ago
2024-10-05 8:37am
v9
· a month ago
2024-09-30 12:40pm
v8
· 2 months ago
2024-09-30 9:06am
v7
· 2 months ago
2024-09-30 8:48am
v6
· 2 months ago
2024-09-29 6:19pm
v5
· 2 months ago
2024-09-29 11:56am
v4
· 2 months ago
2024-09-29 8:18am
v3
· 2 months ago
2024-09-28 9:03pm
v2
· 2 months ago
2024-09-28 5:34pm
v1
· 2 months ago
v21
2024-10-31 8:15pm
Generated on Oct 31, 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.
882 Total Images
View All Images
Dataset Split
Train Set 84%
744Images
Valid Set 12%
103Images
Test Set 4%
35Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Histogram Equalization
Augmentations
Outputs per training example: 3
Blur: Up to 0.7px
Noise: Up to 0.38% of pixels