Dataset Versions
Versions
Rice Leaf Disease Dataset for Object Detection v4.2 - with augment x3
v26
· 3 months ago
Rice Leaf Disease Dataset for Object Detection v4.1 - with augment x2
v24
· 3 months ago
Rice Leaf Disease Dataset for Object Detection v3 - 2x augment - with contrast enhancement
v22
· 6 months ago
Rice Leaf Disease Dataset for Object Detection v3.3 - with augment x2
v21
· 6 months ago
Rice Leaf Disease Dataset for Object Detection v3.2 - no augment
v20
· 6 months ago
Rice Leaf Disease Dataset for Object Detection v3.1 - no augment - with contrast enhancement
v19
· 6 months ago
Rice Leaf Disease Dataset for Object Detection v.2 -w- augmentation - 2x-
v18
· 9 months ago
Rice Blast for Image Classification -cropped-
v16
· 9 months ago
Brown Spot for Image Classification -cropped-
v14
· 9 months ago
Bacterial Leaf Blight for Image Classification -cropped-
v13
· 9 months ago
Healthy rice for Image Classification -cropped-
v10
· 10 months ago
v26
Rice Leaf Disease Dataset for Object Detection v4.2 - with augment x3
Generated on Sep 21, 2024
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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.
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3738 Total Images
View All Images
Dataset Split
Train Set 80%
2988Images
Valid Set 10%
375Images
Test Set 10%
375Images
Preprocessing
Auto-Orient: Applied
Resize: Fit (white edges) in 640x640
Filter Null: Require all images to contain annotations.
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
Saturation: Between -25% and +25%
Brightness: Between -10% and +10%