wheat_count Computer Vision Model
Popular Download Formats
YOLO26
TXT annotations and YAML config used with YOLO26.
YOLOv12
TXT annotations and YAML config used with YOLOv12.
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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87532 Total Images
View All Images
Dataset Split
Train Set 87%
75800Images
Valid Set 7%
5964Images
Test Set 6%
5768Images
Preprocessing
Tile: 2 rows x 2 columns
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 5
Flip: Horizontal, Vertical
Rotation: Between -15° and +15°
Brightness: Between -15% and +15%
Blur: Up to 1.5px
Cutout: 3 boxes with 5% size each
Mosaic: Applied
Bounding Box: Crop: 0% Minimum Zoom, 10% Maximum Zoom