Dataset Versions
Versions
2024-10-07 3:56am
v30
· 3 months ago
2024-10-07 3:15am
v29
· 3 months ago
2024-10-06 3:41pm
v28
· 3 months ago
2024-05-23 10:52am
v27
· 7 months ago
2024-05-17 6:51am
v26
· 7 months ago
2024-05-12 3:20pm
v25
· 7 months ago
Stones-Raw
v24
· 7 months ago
Stones-Augmented
v23
· 7 months ago
Augmentedon10-05-24
v22
· 7 months ago
2024-05-10 1:56pm
v21
· 7 months ago
2024-05-10 1:44pm
v20
· 7 months ago
Aishwarya-b-48-t-40-s-40-yolov9
v16
· 8 months ago
Aishwarya-Hetero-40 each-
v15
· 8 months ago
Dhanush1
v14
· 8 months ago
Performing_good
v13
· 8 months ago
heterov9
v11
· 8 months ago
heteroclarity-source unkown
v8
· 8 months ago
homo-bnt..hetero_stones
v7
· 8 months ago
2024-05-03 10:04am
v6
· 8 months ago
2024-05-02 11:40pm
v5
· 8 months ago
2024-05-02 11:25pm
v4
· 8 months ago
2024-05-02 11:38am
v3
· 8 months ago
2024-05-01 6:58pm
v2
· 8 months ago
2024-05-01 6:45pm
v1
· 8 months ago
v16
Aishwarya-b-48-t-40-s-40-yolov9
Generated on May 6, 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.
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306 Total Images
View All Images
Dataset Split
Train Set 88%
269Images
Valid Set 9%
28Images
Test Set 3%
9Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Augmentations
Outputs per training example: 3
Flip: Horizontal
90° Rotate: Clockwise, Counter-Clockwise
Grayscale: Apply to 10% of images
Hue: Between -15° and +15°