Dataset Versions
Versions
2023-04-12 2:10pm
v35
· 2 years ago
2023-04-12 2:10pm
v34
· 2 years ago
2023-04-12 2:10pm
v33
· 2 years ago
2023-04-12 2:10pm
v32
· 2 years ago
2023-04-12 2:10pm
v31
· 2 years ago
2023-04-12 2:07pm
v30
· 2 years ago
2023-04-09 10:20pm
v29
· 2 years ago
2023-04-09 10:18pm
v28
· 2 years ago
2023-04-09 8:30pm
v27
· 2 years ago
2023-04-09 7:48pm
v26
· 2 years ago
2023-04-09 7:39pm
v25
· 2 years ago
2023-04-09 5:47pm
v24
· 2 years ago
2023-04-09 5:18pm
v23
· 2 years ago
2023-04-09 12:50pm
v22
· 2 years ago
2023-04-09 12:49pm
v21
· 2 years ago
2023-04-09 9:54am
v20
· 2 years ago
2023-04-09 9:14am
v19
· 2 years ago
2023-04-09 8:50am
v18
· 2 years ago
2023-04-05 7:05pm
v17
· 2 years ago
2023-04-05 6:52pm
v16
· 2 years ago
2023-04-05 6:49pm
v15
· 2 years ago
2023-04-05 5:53pm
v14
· 2 years ago
2023-04-05 5:40pm
v13
· 2 years ago
2023-04-04 10:45pm
v12
· 2 years ago
2023-04-04 10:07pm
v11
· 2 years ago
2023-04-04 8:54pm
v10
· 2 years ago
2023-04-04 7:42pm
v9
· 2 years ago
2023-04-02 9:27pm
v8
· 2 years ago
2023-04-02 8:09pm
v7
· 2 years ago
2023-04-02 6:01pm
v6
· 2 years ago
2023-04-02 5:04pm
v5
· 2 years ago
2023-04-01 9:13pm
v4
· 2 years ago
2023-04-01 7:04pm
v3
· 2 years ago
2023-04-01 3:40pm
v2
· 2 years ago
2023-04-01 1:02pm
v1
· 2 years ago
v15
2023-04-05 6:49pm
Generated on Apr 5, 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.
603 Total Images
View All Images
Dataset Split
Train Set 88%
531Images
Valid Set 8%
50Images
Test Set 4%
22Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Histogram Equalization
Filter Null: Require at least 4% of images to contain annotations.
Augmentations
Outputs per training example: 3
Grayscale: Apply to 23% of images
Saturation: Between -25% and +25%
Brightness: Between -20% and +20%
Bounding Box: Flip: Horizontal, Vertical