Dataset Versions
Versions
2024-05-25 4:01pm
v27
· 6 months ago
2024-04-28 6:08am
v26
· 7 months ago
2024-04-28 12:29am
v25
· 7 months ago
2024-04-27 8:23pm
v24
· 7 months ago
2024-04-25 9:02pm
v23
· 7 months ago
2024-04-25 6:53pm
v22
· 7 months ago
2024-04-25 2:27pm
v21
· 7 months ago
2024-04-24 9:19pm
v20
· 7 months ago
2024-04-24 9:13pm
v19
· 7 months ago
2024-04-22 1:12pm
v18
· 7 months ago
2024-04-20 12:55pm
v17
· 7 months ago
2024-04-17 7:05pm
v16
· 7 months ago
2024-04-17 4:59pm
v15
· 7 months ago
kecuali koran
v14
· 8 months ago
2024-03-30 1:36pm
v13
· 8 months ago
kontras-besi
v12
· 8 months ago
full besi
v11
· 8 months ago
belum besi revisi
v10
· 8 months ago
2024-03-29 4:45pm
v9
· 8 months ago
2024-03-16 11:08pm
v8
· 8 months ago
2024-03-16 4:58pm
v5
· 8 months ago
2024-03-16 1:05pm
v4
· 8 months ago
2024-03-09 1:13pm
v2
· 9 months ago
2024-03-09 12:22pm
v1
· 9 months ago
v13
2024-03-30 1:36pm
Generated on Mar 30, 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.
349 Total Images
View All Images
Dataset Split
Train Set 87%
305Images
Valid Set 9%
30Images
Test Set 4%
14Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down