Dataset Versions
Versions
raw_data_without_augmentation
v39
· a year ago
raw_data_with_augmentation
v38
· a year ago
tiled_data_without_augmentation
v37
· a year ago
tiled_data_with_augmentation
v34
· a year ago
2023-08-05 11:10pm
v33
· a year ago
2023-08-05 10:54pm
v32
· a year ago
coba ini
v31
· a year ago
2023-08-05 1:39am
v30
· a year ago
2023-08-05 1:02am
v29
· a year ago
2023-08-05 12:57am
v28
· a year ago
2023-08-05 12:52am
v27
· a year ago
2023-08-05 12:21am
v26
· a year ago
2023-08-05 12:09am
v25
· a year ago
2023-08-05 12:06am
v24
· a year ago
2023-08-04 5:19pm
v23
· a year ago
2023-08-04 4:55pm
v18
· a year ago
2023-08-04 4:36pm
v14
· a year ago
2023-08-04 4:28pm
v13
· a year ago
2023-07-27 9:12am
v12
· a year ago
data_skripsi
v11
· 2 years ago
2023-05-09 7:29pm
v10
· 2 years ago
2023-05-09 7:22pm
v9
· 2 years ago
2023-05-09 7:12pm
v8
· 2 years ago
2023-05-07 10:43am
v7
· 2 years ago
2023-03-27 12:39am
v6
· 2 years ago
2023-03-08 9:37pm
v4
· 2 years ago
v29
2023-08-05 1:02am
Generated on Aug 4, 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.
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3047 Total Images
View All Images
Dataset Split
Train Set 87%
2652Images
Valid Set 6%
188Images
Test Set 7%
207Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Tile: 2 rows x 2 columns
Filter Null: Require at least 95% of images to contain annotations.
Augmentations
Outputs per training example: 3
Rotation: Between -5° and +5°
Saturation: Between -15% and +15%
Blur: Up to 1px
Noise: Up to 1% of pixels