Dataset Versions
Versions
aug2-mixed
v46
· 8 months ago
aug2-styrofoam-only
v45
· 8 months ago
aug2-rags-only
v44
· 8 months ago
aug2-plastic_wrapper-only
v43
· 8 months ago
aug2-plastic_cup-only
v42
· 8 months ago
aug2-plastic_bottle-only
v41
· 8 months ago
aug2-plastic_bag-only
v40
· 8 months ago
aug2-paper_cup-only
v39
· 8 months ago
aug2-paper_container-only
v38
· 8 months ago
aug2-paper_bag-only
v37
· 8 months ago
aug2-metal_can-only
v36
· 8 months ago
aug2-glass_bottle-only
v35
· 8 months ago
aug2-face_mask-only
v34
· 8 months ago
aug2-drink_carton-only
v33
· 8 months ago
aug2-cardboard-only
v32
· 8 months ago
aug-mixed
v15
· 9 months ago
aug-styrofoam-only
v14
· 9 months ago
aug-rags-only
v13
· 9 months ago
aug-plastic_wrapper-only
v12
· 9 months ago
aug-plastic_cup-only
v11
· 9 months ago
aug-plastic_bottle-only
v10
· 9 months ago
aug-plastic_bag-only
v9
· 9 months ago
aug-paper_cup-only
v8
· 9 months ago
aug-paper_container-only
v7
· 9 months ago
aug-paper_bag-only
v6
· 9 months ago
aug-metal_can-only
v5
· 9 months ago
aug-glass_bottle-only
v4
· 9 months ago
aug-face_mask-only
v3
· 9 months ago
aug-drink_carton-only
v2
· 9 months ago
aug-cardboard-only
v1
· 9 months ago
v46
aug2-mixed
Generated on May 11, 2024
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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.
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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2592 Total Images
View All Images
Dataset Split
Train Set 94%
2443Images
Valid Set 4%
99Images
Test Set 2%
50Images
Preprocessing
Auto-Orient: Applied
Resize: Fit within 640x640
Filter Null: Require all images to contain annotations.
Filter by Tag: 1 required, 0 dropped
Augmentations
Outputs per training example: 7
Flip: Horizontal, Vertical
90° Rotate: Clockwise, Counter-Clockwise, Upside Down
Rotation: Between -15° and +15°
Shear: ±10° Horizontal, ±10° Vertical
Grayscale: Apply to 15% of images
Brightness: Between -15% and +15%