Dataset Versions
Versions
2023-05-24 6:55pm
v33
· a year ago
2022-08-27 8:22pm
v32
· 2 years ago
gun dataset may_05_22_2nd
v31
· 3 years ago
gun dataset_may_05_22
v30
· 3 years ago
gun dataset_apr_29_22_2nd
v29
· 3 years ago
Rachel test 2022-04-29 11:34am
v28
· 3 years ago
gun dataset_apr_30_22
v27
· 3 years ago
gun dataset_apr_29_22
v26
· 3 years ago
gun dataset_apr_28_22
v25
· 3 years ago
gun dataset_apr_22_22
v24
· 3 years ago
gun dataset_apr_17_22
v23
· 3 years ago
gun dataset_apr_15_22_edit
v21
· 3 years ago
gun dataset_apr_15_22-4-
v20
· 3 years ago
gun dataset_apr_15_22 -3-
v19
· 3 years ago
gun dataset_apr_15_22-2-
v18
· 3 years ago
gun dataset_apr_15_22
v17
· 3 years ago
gun dataset_apr_12_22
v16
· 3 years ago
gun dataset_apr_08_22 -2-
v15
· 3 years ago
gun dataset_apr_08_22
v14
· 3 years ago
gun dataset_apr_07_22_2
v13
· 3 years ago
gun dataset_apr_07_22
v10
· 3 years ago
gun dataset_mar_25_22
v9
· 3 years ago
gun dataset_mar_16_22
v8
· 3 years ago
gun dataset_mar_03_22
v7
· 3 years ago
gun dataset_feb_24_22
v6
· 3 years ago
gun dataset_feb_22_22
v5
· 3 years ago
gun dataset_feb_18_22
v4
· 3 years ago
gun dataset_feb_17_22 -2-
v3
· 3 years ago
gun dataset_feb_17_22
v2
· 3 years ago
gun dataset_feb_16_22
v1
· 3 years ago
v1
gun dataset_feb_16_22
Generated on Feb 16, 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 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.
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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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514 Total Images
View All Images
Dataset Split
Train Set 88%
450Images
Valid Set 8%
42Images
Test Set 4%
22Images
Preprocessing
Auto-Orient: Applied
Resize: Stretch to 416x416
Augmentations
Outputs per training example: 3
Flip: Horizontal
Crop: 0% Minimum Zoom, 20% Maximum Zoom
Rotation: Between -15° and +15°
Blur: Up to 10px
Noise: Up to 5% of pixels
Cutout: 3 boxes with 10% size each