Dataset Versions
Versions
2024-10-29 11:39pm
v25
· 16 days ago
2024-10-29 3:43pm
v24
· 16 days ago
2024-10-29 3:42pm
v23
· 16 days ago
2024-10-28 11:59pm
v22
· 17 days ago
2024-10-28 11:08pm
v21
· 17 days ago
2024-10-28 3:12pm
v20
· 17 days ago
2024-10-27 11:08pm
v19
· 18 days ago
2024-10-27 11:02pm
v18
· 18 days ago
2024-10-27 10:26pm
v17
· 18 days ago
2024-10-27 7:15pm
v16
· 18 days ago
2024-10-27 3:52pm
v15
· 18 days ago
2024-10-27 3:49pm
v14
· 18 days ago
2024-10-27 2:32pm
v13
· 18 days ago
2024-10-27 12:51pm
v12
· 18 days ago
2024-10-26 6:58pm
v11
· 19 days ago
2024-10-26 6:11pm
v10
· 19 days ago
2024-10-24 3:37pm
v9
· 21 days ago
2024-10-23 3:38pm
v8
· 22 days ago
2024-10-22 3:58pm
v7
· 23 days ago
2024-10-21 3:45pm
v6
· 24 days ago
2024-10-12 6:07pm
v5
· a month ago
2024-10-12 5:12pm
v4
· a month ago
2024-10-12 5:11pm
v3
· a month ago
2024-10-12 2:58pm
v2
· a month ago
2024-09-28 1:11am
v1
· 2 months ago
v10
2024-10-26 6:11pm
Generated on Oct 26, 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.
1201 Total Images
View All ImagesDataset Split
Train Set 87%
1050Images
Valid Set 8%
101Images
Test Set 4%
50Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 3
Grayscale: Apply to 15% of images
Saturation: Between -20% and +20%
Noise: Up to 0.3% of pixels