Dataset Versions
Versions
Primer proyecto
v34
· 2 years ago
Proyecto 1
v33
· 2 years ago
No daug 1664
v32
· 2 years ago
Trainset normal daug 1664
v31
· 2 years ago
2022-06-06 1:43pm
v30
· 2 years ago
Trainset bbox daug 1664
v29
· 2 years ago
Validset no daug 416 22 img
v28
· 2 years ago
trainset normal daug 416 88 img
v27
· 2 years ago
trainset bbox daug 416 88 img
v26
· 2 years ago
Trainset Bbox Daug 1600 88 img
v25
· 3 years ago
Trainset normal Daug 1600 88 img
v24
· 3 years ago
Dataset normal Daug 1600 72 img
v23
· 3 years ago
Train set no data aug split by 4 416
v22
· 3 years ago
Train set split by 4 416 Normal DAug No noise
v21
· 3 years ago
Dataset split by 4 416 Normal DAug NO noise
v20
· 3 years ago
Dataset split by 4 416 Normal DAug
v19
· 3 years ago
Dataset split by 4 All Bbox 416
v18
· 3 years ago
Dataset split by 4 All Bbox
v16
· 3 years ago
Dataset 220 img All Bbox
v15
· 3 years ago
Dataset NoDAug 1664 22 img
v13
· 3 years ago
Dataset Aug All Bbox 1664
v12
· 3 years ago
Dataset Aug All Bbox 1600
v11
· 3 years ago
Dataset Aug Rotate 1600
v10
· 3 years ago
Dataset Aug Crop 1600
v9
· 3 years ago
Dataset Aug 90 rotate 1600
v8
· 3 years ago
D N Aug 1600 22img
v7
· 3 years ago
2022-04-04 2:11pm
v6
· 3 years ago
Dataset Aug Flip 1600
v5
· 3 years ago
Dataset No DAug 1600
v4
· 3 years ago
Dataset No DAug
v3
· 3 years ago
2022-04-01 7:53pm
v2
· 3 years ago
2022-04-01 7:52pm
v1
· 3 years ago
v26
trainset bbox daug 416 88 img
Generated on Jun 5, 2022
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YOLOv11
TXT annotations and YAML config used with YOLOv11.
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TXT annotations and YAML config used with YOLOv8.
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TXT annotations and YAML config used with YOLOv5.
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TXT annotations and YAML config used with YOLOv7.
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COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.
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Darknet TXT annotations used with YOLO Darknet (both v3 and v4) and YOLOv3 PyTorch.
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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.
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PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.
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66 Total Images
View All Images
Dataset Split
Train Set 100%
66Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Resize: Fit (black edges) in 416x416
Augmentations
Outputs per training example: 3
Bounding Box: Flip: Horizontal, Vertical
Bounding Box: 90° Rotate: Clockwise, Counter-Clockwise
Bounding Box: Crop: 0% Minimum Zoom, 20% Maximum Zoom
Bounding Box: Rotation: Between -15° and +15°
Bounding Box: Shear: ±15° Horizontal, ±15° Vertical
Bounding Box: Brightness: Between -30% and +30%
Bounding Box: Exposure: Between -10% and +10%
Bounding Box: Blur: Up to 1.25px