Dataset Versions
Versions
2024-06-13 2:31pm
v31
· 5 months ago
2024-06-13 2:12pm
v30
· 5 months ago
AddedClasses
v29
· 5 months ago
AugmentationV2
v27
· 6 months ago
AugmentationV1
v26
· 6 months ago
CorrectingDoors
v25
· 7 months ago
AddingTextClass
v24
· 8 months ago
2ndTermTrial3
v23
· 9 months ago
2024-03-01 9:36pm
v22
· 9 months ago
2ndTermTrial2
v21
· 9 months ago
2ndTermTrial1
v19
· 9 months ago
2024-02-23 10:12pm
v18
· 9 months ago
2024-02-22 5:11pm
v16
· 9 months ago
2023-12-13 6:12pm
v14
· a year ago
Version3-Moataz
v13
· a year ago
Version3-doorsFocus-Shaimaa
v11
· a year ago
Version2-Shaimaa-SolvedRotated
v8
· a year ago
2023-12-10 4:35pm
v7
· a year ago
2023-12-10 4:25pm
v6
· a year ago
Version2-Moataz
v5
· a year ago
Version1-Moataz
v4
· a year ago
Version1-Shaimaa
v3
· a year ago
v29
AddedClasses
Generated on Jun 9, 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.
Other Formats
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5563 Total Images
View All Images
Dataset Split
Train Set 87%
4854Images
Valid Set 8%
430Images
Test Set 5%
279Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Modify Classes: 6 remapped, 8 dropped
Filter Null: Require all images to contain annotations.
Filter by Tag: 0 required, 2 dropped
Augmentations
Outputs per training example: 2
Brightness: Between -20% and +20%
Blur: Up to 1px
Noise: Up to 1.8% of pixels