Dataset Versions
Versions
Grayscale- Apply to 25- of images
v22
· 2 years ago
2023-03-19 1:52am
v21
· 2 years ago
2023-03-18 4:44pm
v20
· 2 years ago
Bounding Box- Flip- Horizontal- Vertical
v19
· 2 years ago
Bounding Box- Brightness- Between -30- and -30-
v18
· 2 years ago
Mosaic x2
v17
· 2 years ago
Mosaic x1
v16
· 2 years ago
Cutout- 5 boxes with 10- size each
v15
· 2 years ago
Flip- Horizontal- Vertical
v14
· 2 years ago
Brightness- Between -30- and -30-
v13
· 2 years ago
2023-03-16 5:12pm
v12
· 2 years ago
2023-03-16 5:10pm
v11
· 2 years ago
2023-03-17 2:04am
v10
· 2 years ago
non-augmented 2C RGB
v8
· 2 years ago
flip rotaiation plur cutout 640 2C
v7
· 2 years ago
640_Mosaic 2C
v6
· 2 years ago
640_non-augmented 2C
v5
· 2 years ago
flip rotaiation plur cutout 640 1C
v4
· 2 years ago
640_Mosaic
v3
· 2 years ago
320_non-augmented
v2
· 2 years ago
640_non-augmented
v1
· 2 years ago
v21
2023-03-19 1:52am
Generated on Mar 18, 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
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2001 Total Images
View All Images
Dataset Split
Train Set 92%
1836Images
Valid Set 5%
108Images
Test Set 3%
57Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Modify Classes: 4 remapped, 3 dropped
Augmentations
Outputs per training example: 3
Mosaic: Applied