Dataset Versions
Versions
2023-02-21 2:11pm
v29
· 2 years ago
2023-02-21 10:50am
v28
· 2 years ago
2023-02-21 10:47am
v27
· 2 years ago
2023-02-21 9:37am
v26
· 2 years ago
2023-02-21 9:37am
v25
· 2 years ago
2023-02-21 9:36am
v24
· 2 years ago
2023-02-20 2:53pm
v23
· 2 years ago
2023-02-20 12:13pm
v22
· 2 years ago
2023-02-20 12:13pm
v21
· 2 years ago
2023-02-20 12:13pm
v20
· 2 years ago
2023-02-20 12:13pm
v19
· 2 years ago
2023-02-20 12:13pm
v18
· 2 years ago
2023-02-20 12:13pm
v17
· 2 years ago
2023-02-20 12:13pm
v16
· 2 years ago
2023-02-20 12:13pm
v15
· 2 years ago
2023-02-20 12:12pm
v14
· 2 years ago
2023-02-20 10:30am
v13
· 2 years ago
2023-02-20 10:29am
v12
· 2 years ago
2023-02-20 10:28am
v11
· 2 years ago
2023-02-20 10:15am
v10
· 2 years ago
2023-02-19 9:48am
v9
· 2 years ago
2023-02-19 9:48am
v8
· 2 years ago
2023-02-19 9:46am
v7
· 2 years ago
2023-02-19 9:46am
v6
· 2 years ago
2023-02-19 9:45am
v5
· 2 years ago
2023-02-19 9:42am
v4
· 2 years ago
2023-02-19 9:40am
v3
· 2 years ago
2023-02-19 9:35am
v2
· 2 years ago
2023-02-19 9:23am
v1
· 2 years ago
v26
2023-02-21 9:37am
Generated on Feb 21, 2023
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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81 Total Images
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Dataset Split
Train Set 100%
81Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
No preprocessing steps were applied.
Augmentations
Outputs per training example: 1
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