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Fruit Disease and Treatment
Object Detection
Overview
Images
4499
Dataset
3
Model
1
API Docs
Analytics
Dataset Versions
Try Pre-Trained Model
Versions
FDTv3
v3
·
2 months ago
FDTv2
v2
·
2 months ago
Fruits_Disease_Ver1
v1
·
5 months ago
v3
FDTv3
Generated on Dec 19, 2024
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Dataset
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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10797
Total Images
View All Images
Dataset Split
Train Set
87%
9447
Images
Valid Set
8%
900
Images
Test Set
4%
450
Images
Preprocessing
Auto-Orient:
Applied
Static Crop:
25-75% Horizontal Region, 25-75% Vertical Region
Resize:
Stretch to 640x640
Auto-Adjust Contrast:
Using Contrast Stretching
Augmentations
Outputs per training example:
3
Flip:
Horizontal, Vertical
90° Rotate:
Clockwise, Counter-Clockwise
Shear:
±10° Horizontal, ±10° Vertical
Saturation:
Between -25% and +25%
Brightness:
Between -15% and +15%
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