Dataset Versions
Versions
V4 - Haider Annotation is included - Previous Augmentation only
v22
· a year ago
V3.1 - Faizan images is updated- Previous Accepted Augmentation is applied
v21
· a year ago
V2.1.3 - Appropriate augmentation is selected
v20
· a year ago
mosaic augmentation is applied- but left it for now
v19
· a year ago
2023-10-20 12:12am
v18
· a year ago
2023-10-19 11:54pm
v17
· a year ago
2023-10-19 11:42pm
v16
· a year ago
2023-10-19 11:39pm
v15
· a year ago
2023-10-19 11:37pm
v14
· a year ago
2023-10-19 11:27pm
v13
· a year ago
2023-10-19 11:24pm
v12
· a year ago
2023-10-19 10:16pm
v10
· a year ago
V2.1.2 - Ashar Images and original Images_ Multiple Augmentations tryout
v9
· a year ago
V2.1 - Ashar Images and original Images_ no Augment
v8
· a year ago
V2 - Original annotation only__Augmentation Applied 3x
v7
· a year ago
V1 - Original Annotation is remapped to TRASH
v6
· a year ago
V0 - Before Custom Annotation
v4
· a year ago
2023-10-18 8:10pm
v2
· a year ago
v10
2023-10-19 10:16pm
Generated on Oct 19, 2023
Popular Download Formats
YOLOv11
TXT annotations and YAML config used with YOLOv11.
YOLOv9
TXT annotations and YAML config used with YOLOv9.
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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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CreateML JSON format is used with Apple's CreateML and Turi Create tools.
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213 Total Images
View All Images
Dataset Split
Train Set 100%
213Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Modify Classes: 1 remapped, 0 dropped
Augmentations
Outputs per training example: 3
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Noise: Up to 2% of pixels