AI deep learning on Metal AM Steel defects detection Image Dataset
Versions
2024-05-16 3:24pm
v12
May 16, 2024
2024-03-29 5:59pm
v11
Mar 29, 2024
2024-03-29 5:14pm
v10
Mar 29, 2024
2023-12-18 9:52am
v9
Dec 18, 2023
2023-11-20 10:58am
v8
Nov 20, 2023
2023-11-18 8:53pm
v7
Nov 18, 2023
2023-10-30 5:53pm
v6
Oct 30, 2023
2023-08-29 10:22pm
v5
Aug 29, 2023
2023-08-28 9:58am
v4
Aug 28, 2023
2023-08-27 11:55am
v3
Aug 27, 2023
2023-08-27 11:50am
v2
Aug 27, 2023
v2
2023-08-27 11:50am
Generated on Aug 27, 2023
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YOLOv9
TXT annotations and YAML config used with YOLOv9.
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TXT annotations and YAML config used with YOLOv8.
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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.
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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.
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PaliGemma JSONL format used for fine-tuning PaliGemma, Google's open multimodal vision model.
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414 Total Images
View All Images
Dataset Split
Train Set 88%
363Images
Valid Set 8%
34Images
Test Set 4%
17Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Grayscale: Applied
Augmentations
Outputs per training example: 3
Brightness: Between -25% and +25%
Bounding Box: Brightness: Between -25% and +25%
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