Dataset Versions
Versions
2024-01-05 2:12pm
v27
· a year ago
2023-12-02 10:36pm
v26
· a year ago
2023-12-02 10:21pm
v25
· a year ago
2023-12-01 1:10pm
v24
· a year ago
2023-11-30 7:44pm
v23
· a year ago
hanya tampak depan
v22
· a year ago
tampak depan
v19
· a year ago
2023-11-28 10:02pm
v18
· a year ago
2023-11-25 4:24pm
v17
· a year ago
2023-11-23 8:41pm
v16
· a year ago
2023-11-23 4:27am
v15
· a year ago
2023-11-22 7:21pm
v14
· a year ago
2023-11-22 4:16pm
v13
· a year ago
2023-11-22 12:34am
v12
· a year ago
2023-11-22 12:54am
v11
· a year ago
2023-11-21 9:41pm
v10
· a year ago
2023-11-21 1:26pm
v9
· a year ago
2023-11-21 1:22pm
v8
· a year ago
2023-11-20 8:20pm
v7
· a year ago
2023-11-17 12:37am
v6
· a year ago
2023-11-15 7:44pm
v5
· a year ago
2023-11-14 2:50am
v4
· a year ago
2023-11-09 8:08pm
v3
· a year ago
2023-11-09 4:28am
v2
· a year ago
2023-11-09 4:22am
v1
· a year ago
v27
2024-01-05 2:12pm
Generated on Jan 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
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3500 Total Images
View All Images
Dataset Split
Train Set 88%
3090Images
Valid Set 8%
272Images
Test Set 4%
138Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Histogram Equalization
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Shear: ±15° Horizontal, ±15° Vertical
Brightness: Between -25% and +25%