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yolooooooo
Object Detection
Overview
Images
127
Dataset
21
Model
1
API Docs
Analytics
Dataset Versions
Try Pre-Trained Model
Versions
2024-12-10 11:17am
v23
·
a month ago
2024-12-10 10:29am
v22
·
a month ago
2024-12-10 10:18am
v21
·
a month ago
2024-12-10 9:25am
v20
·
a month ago
2024-12-10 8:44am
v19
·
a month ago
2024-12-09 11:33pm
v18
·
a month ago
2024-12-09 11:07pm
v17
·
a month ago
2024-12-09 10:05pm
v16
·
a month ago
2024-12-09 9:45pm
v15
·
a month ago
2024-12-09 9:15pm
v14
·
a month ago
2024-12-09 8:58pm
v13
·
a month ago
2024-12-09 8:43pm
v12
·
a month ago
2024-12-09 8:20pm
v11
·
a month ago
2024-12-09 8:18pm
v9
·
a month ago
2024-12-09 8:16pm
v8
·
a month ago
2024-12-09 8:16pm
v7
·
a month ago
2024-12-09 8:15pm
v6
·
a month ago
2024-12-09 7:11pm
v5
·
a month ago
2024-12-09 6:25pm
v4
·
a month ago
2024-12-05 4:19pm
v3
·
a month ago
2024-11-28 4:10pm
v1
·
a month ago
v14
2024-12-09 9:15pm
Generated on Dec 9, 2024
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Dataset
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
Choose another format.
301
Total Images
View All Images
Dataset Split
Train Set
87%
261
Images
Valid Set
13%
40
Images
Test Set
%
0
Images
Preprocessing
Auto-Orient:
Applied
Resize:
Fit (black edges) in 640x640
Auto-Adjust Contrast:
Using Adaptive Equalization
Filter Null:
Require at least 60% of images to contain annotations.
Augmentations
Outputs per training example:
3
Flip:
Horizontal, Vertical
90° Rotate:
Clockwise, Counter-Clockwise
Rotation:
Between -15° and +15°
Hue:
Between -7° and +7°
Brightness:
Between -15% and +15%
Blur:
Up to 0.8px
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