Cigarrette_Detection Image Dataset
Versions
2022-07-19 9:32pm
v30
Jul 19, 2022
2022-07-17 4:27pm
v29
Jul 17, 2022
2022-07-05 8:52pm
v28
Jul 5, 2022
2022-07-05 5:09pm
v27
Jul 5, 2022
2022-07-05 5:04pm
v26
Jul 5, 2022
2022-07-05 10:24am
v25
Jul 5, 2022
2022-05-16 12:40pm
v24
May 16, 2022
2022-05-15 5:47pm
v23
May 15, 2022
2022-05-12 8:34pm
v22
May 12, 2022
2022-05-09 2:53pm
v21
May 9, 2022
2022-05-09 8:11am
v20
May 9, 2022
2022-05-08 1:50pm
v19
May 8, 2022
2022-05-08 1:33pm
v18
May 8, 2022
2022-05-05 9:12pm
v17
May 5, 2022
2022-05-04 7:02am
v16
May 4, 2022
2022-05-03 7:07am
v15
May 3, 2022
2022-05-02 5:36pm
v14
May 2, 2022
2022-05-02 7:11am
v13
May 2, 2022
2022-05-02 7:05am
v12
May 2, 2022
2022-05-01 3:58pm
v11
May 1, 2022
2022-05-01 1:39pm
v10
May 1, 2022
2022-05-01 1:27pm
v9
May 1, 2022
2022-05-01 7:54am
v8
May 1, 2022
2022-04-30 5:56pm
v7
Apr 30, 2022
2022-04-30 4:56pm
v6
Apr 30, 2022
2022-04-29 9:35pm
v5
Apr 29, 2022
2022-04-28 10:17pm
v4
Apr 28, 2022
2022-04-28 12:33pm
v3
Apr 28, 2022
2022-04-28 12:30pm
v2
Apr 28, 2022
2022-04-28 7:14am
v1
Apr 28, 2022
v8
2022-05-01 7:54am
Generated on May 1, 2022
Popular Download Formats
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.
MT-YOLOv6
MT-YOLOv6 TXT annotations used with meituan/YOLOv6.
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.
CreateML JSON
CreateML JSON format is used with Apple's CreateML and Turi Create tools.
Other Formats
Choose another format.
1288 Total Images
View All Images
Dataset Split
Train Set 97%
1248Images
Valid Set %
0Images
Test Set 3%
40Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 416x416
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 20% Maximum Zoom
Noise: Up to 5% of pixels
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