Dataset Versions
Versions
2024-02-05 5:02pm
v36
· 10 months ago
2024-02-05 4:55pm
v35
· 10 months ago
2024-02-05 4:18pm
v34
· 10 months ago
2024-02-05 4:14pm
v33
· 10 months ago
2024-02-05 4:10pm
v32
· 10 months ago
2024-02-05 4:08pm
v31
· 10 months ago
2024-02-05 3:58pm
v30
· 10 months ago
2024-02-05 3:43pm
v29
· 10 months ago
2024-02-05 3:40pm
v28
· 10 months ago
2024-02-05 3:34pm
v27
· 10 months ago
2024-02-05 2:54pm
v26
· 10 months ago
2024-02-05 2:50pm
v25
· 10 months ago
2024-02-05 2:47pm
v24
· 10 months ago
2024-02-05 2:35pm
v23
· 10 months ago
2024-02-05 2:24pm
v22
· 10 months ago
2024-02-05 2:21pm
v21
· 10 months ago
2024-02-05 1:52pm
v19
· 10 months ago
2024-02-05 1:51pm
v18
· 10 months ago
2024-02-05 1:49pm
v17
· 10 months ago
2024-02-05 1:32pm
v16
· 10 months ago
2024-02-05 11:38am
v15
· 10 months ago
2024-02-05 11:10am
v13
· 10 months ago
2024-02-05 11:08am
v12
· 10 months ago
2024-02-05 11:04am
v11
· 10 months ago
2024-02-05 1:19am
v9
· 10 months ago
2024-02-02 9:32am
v7
· 10 months ago
2024-02-01 10:59pm
v6
· 10 months ago
2024-02-01 10:40pm
v5
· 10 months ago
v25
2024-02-05 2:50pm
Generated on Feb 5, 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.
980 Total Images
View All Images
Dataset Split
Train Set 100%
980Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Static Crop: 9-91% Horizontal Region, 46-85% Vertical Region
Resize: Fill (with center crop) in 426x780
Tile: 2 rows x 5 columns
Modify Classes: 0 remapped, 2 dropped
Augmentations
Outputs per training example: 3
Brightness: Between -10% and +10%
Exposure: Between -5% and +5%