Dataset Versions
Versions
AdvModel-Mosaic-v8m-900imgsz
v37
· a year ago
ArtisanModel-Flip-v8m-900imgsz
v36
· a year ago
Yolov8 Images
v35
· a year ago
ArtisanModel-Blur-v8m-50PercentNull
v34
· a year ago
SimpleModel-BrightColors-v8m
v33
· a year ago
AdvModel-Mosaic-v8m
v32
· a year ago
ArtisanModel-Flip-v8m
v30
· a year ago
2023-10-28 4:39pm
v29
· a year ago
2023-10-28 4:37pm
v28
· a year ago
ArtisanModel-Blur-v8-50PercentNull
v27
· a year ago
ArtisanModel-BB aug-v8
v26
· a year ago
ArtisanModel-Blur-v8
v24
· a year ago
ArtisanModel-Noise-v8
v23
· a year ago
ArtisanModel-Expose-v8
v22
· a year ago
ArtisanModel-Bright-v8
v21
· a year ago
ArtisanModel-Crop-v8
v20
· a year ago
ArtisanModel-Rotate-v8
v19
· a year ago
ArtisanModel-Flip-v8
v17
· a year ago
AdvModel-Mosaic-v8
v16
· a year ago
AdvModel-Noise-v8
v15
· a year ago
AdvModel-Shear-v8
v14
· a year ago
AdvModel-Crop-v8
v13
· a year ago
AdvModel-Rotate-v8
v12
· a year ago
SimpleModel-Isolate-v8
v10
· a year ago
SimpleModel-Filtered-v8
v8
· a year ago
yolov8x 100
v7
· a year ago
SimpleModel-BrightColors-v8
v6
· a year ago
try yolov8s
v5
· a year ago
SimpleModel-Flip-v5
v4
· a year ago
SimpleModel-Flip-v8
v3
· a year ago
Tile-2x2- 9-23-2023
v2
· a year ago
v12
AdvModel-Rotate-v8
Generated on Sep 26, 2023
Popular Download Formats
YOLOv11
TXT annotations and YAML config used with YOLOv11.
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TXT annotations and YAML config used with YOLOv9.
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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.
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558 Total Images
View All ImagesDataset Split
Train Set 86%
480Images
Valid Set 9%
53Images
Test Set 4%
25Images
Preprocessing
Auto-Orient: Applied
Resize: Fit (white edges) in 640x640
Auto-Adjust Contrast: Using Adaptive Equalization
Grayscale: Applied
Modify Classes: 1 remapped, 0 dropped
Filter Null: Require at least 75% of images to contain annotations.
Augmentations
Outputs per training example: 3
Flip: Horizontal
90° Rotate: Clockwise, Counter-Clockwise
Rotation: Between -17° and +17°
Brightness: Between -20% and +20%
Exposure: Between -15% and +15%