Benih Padi Image Dataset
Versions
2024-05-28 1:40pm
v27
May 28, 2024
2024-05-06 1:09pm
v26
May 6, 2024
2024-05-05 6:54pm
v25
May 5, 2024
2024-04-26 8:53am
v24
Apr 26, 2024
2024-04-26 7:42am
v23
Apr 26, 2024
2024-04-26 7:32am
v22
Apr 26, 2024
2024-04-25 5:46am
v21
Apr 24, 2024
2024-04-25 2:49am
v20
Apr 24, 2024
2024-04-25 12:34am
v19
Apr 24, 2024
2024-04-25 12:31am
v18
Apr 24, 2024
2024-04-25 12:23am
v17
Apr 24, 2024
2024-04-24 2:40pm
v16
Apr 24, 2024
2024-04-24 2:08pm
v15
Apr 24, 2024
2024-04-23 9:31am
v14
Apr 23, 2024
2024-03-20 2:28pm
v13
Mar 20, 2024
2024-03-20 2:19pm
v12
Mar 20, 2024
2024-03-19 9:42pm
v11
Mar 19, 2024
2024-03-19 8:07am
v10
Mar 19, 2024
2024-03-19 7:51am
v9
Mar 19, 2024
2024-03-16 8:45am
v8
Mar 16, 2024
2024-03-15 2:06pm
v7
Mar 15, 2024
2024-03-15 2:05pm
v6
Mar 15, 2024
2024-03-15 11:54am
v5
Mar 15, 2024
2024-03-07 1:30pm
v4
Mar 7, 2024
2024-03-07 1:21pm
v3
Mar 7, 2024
2024-03-06 1:55pm
v2
Mar 6, 2024
2024-03-06 1:45pm
v1
Mar 6, 2024
v27
2024-05-28 1:40pm
Generated on May 28, 2024
Popular Download Formats
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.
871 Total Images
View All Images
Dataset Split
Train Set 89%
771Images
Valid Set 10%
84Images
Test Set 2%
16Images
Preprocessing
Auto-Orient: Applied
Resize: Fit within 640x640
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -15° and +15°
Saturation: Between -17% and +17%
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