NAI_BHQ_Laminectomy_Set_1 Image Dataset
Versions
2022-09-18 10:39pm
v15
Sep 18, 2022
2022-09-18 10:38pm
v14
Sep 18, 2022
2022-09-18 10:38pm
v13
Sep 18, 2022
2022-09-18 10:37pm
v12
Sep 18, 2022
2022-09-18 10:36pm
v11
Sep 18, 2022
2022-09-18 10:35pm
v10
Sep 18, 2022
2022-09-18 10:35pm
v9
Sep 18, 2022
2022-09-18 10:32pm
v8
Sep 18, 2022
2022-09-18 10:30pm
v7
Sep 18, 2022
2022-09-18 10:28pm
v6
Sep 18, 2022
2022-09-18 10:27pm
v5
Sep 18, 2022
2022-09-18 10:27pm
v4
Sep 18, 2022
2022-09-18 10:26pm
v3
Sep 18, 2022
2022-09-18 10:25pm
v2
Sep 18, 2022
2022-09-18 10:24pm
v1
Sep 18, 2022
v15
2022-09-18 10:39pm
Generated on Sep 18, 2022
Popular Download Formats
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.
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285 Total Images
View All Images
Dataset Split
Train Set 100%
285Images
Valid Set %
0Images
Test Set %
0Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 640x640
Augmentations
Outputs per training example: 3
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Crop: 0% Minimum Zoom, 50% Maximum Zoom
Rotation: Between -15° and +15°
Shear: ±15° Horizontal, ±15° Vertical
Grayscale: Apply to 25% of images
Hue: Between -25° and +25°
Saturation: Between -25% and +25%
Brightness: Between -25% and +25%
Exposure: Between -25% and +25%
Blur: Up to 10px
Noise: Up to 3% of pixels
Cutout: 16 boxes with 15% size each
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