Dataset Versions
Versions
2022-06-09 2:53pm
v28
· 2 years ago
2022-05-10 2:07pm
v27
· 3 years ago
2022-05-07 10:25pm
v26
· 3 years ago
2022-04-10 12:37pm
v25
· 3 years ago
2022-04-10 12:33pm
v24
· 3 years ago
2022-04-10 12:28pm
v23
· 3 years ago
2022-04-10 12:25pm
v22
· 3 years ago
2022-04-09 7:10pm
v21
· 3 years ago
2022-04-09 6:51pm
v20
· 3 years ago
2022-04-09 1:26pm
v19
· 3 years ago
2022-04-09 1:16pm
v18
· 3 years ago
full
v16
· 3 years ago
2022-02-03 -Philipp-
v15
· 3 years ago
2022-02-03 -Stefffafaaananaqn-
v14
· 3 years ago
2022-02-02 -Sascha-
v13
· 3 years ago
2022-01-19 10:55pm
v12
· 3 years ago
2022-01-16 10:50am
v11
· 3 years ago
2022-01-13 2:14pm
v10
· 3 years ago
2022-01-12 7:14pm
v9
· 3 years ago
2021-12-20 6:37pm
v8
· 3 years ago
2021-12-20 6:33pm
v7
· 3 years ago
2021-12-10 2:24pm
v6
· 3 years ago
2021-12-07 5:46pm
v2
· 3 years ago
v8
2021-12-20 6:37pm
Generated on Dec 20, 2021
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.
555 Total Images
View All Images
Dataset Split
Train Set 88%
486Images
Valid Set 8%
45Images
Test Set 4%
24Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 800x800
Filter Null: Require all images to contain annotations.
Augmentations
Outputs per training example: 3
Flip: Horizontal
Grayscale: Apply to 25% of images
Brightness: Between -23% and +23%
Noise: Up to 4% of pixels
Bounding Box: Brightness: Between -25% and +25%